Just one week after John Korir ran a Boston Marathon course record of 2:01:52, Sabastian Sawe and Yomif Kejelcha BOTH finished the London Marathon in under two hours— the first (and second) time it’s ever been accomplished in an actual race. Sawe finished the marathon in 1:59:30, while Kejelcha finished well within eyeshot, just 11 seconds behind at a 1:59:41. Although Kipchoge accomplished this insane feat over 5 years ago in controlled conditions, to me this feels like one of the biggest mental and physical barriers in all of sports has been broken in a way that we may never see again in our lifetimes. Perhaps the fact that the two men who accomplished it were actually racing each other to the finish was what caused it to happen for the first (and certainly not last) time.

The stats from the effort are absolutely insane, and I’d encourage you to take a look at coverage like Runner’s World that puts into perspective how ludicrous of an effort it was. (As a teaser, Sawa averaged 4:33/mile pace for the whole effort which is mind-blowing)

After consuming as much content as I could about the unbelievable achievement of both men, I decided it was worth it to draft an article focused slightly more on some sports technology and science aspects of the feat. After not very much internal deliberation the article turned into a Q&A format. With such a quick turnaround from the actual world record (i.e., yesterday), this article is a bit shorter than normal, but hopefully provides some additional context for the run you haven’t found elsewhere!

I also hope that anyone who watched Sponegebob growing up appreciates this Recess article’s meme— it might be my favorite one I’ve created so far.

Onward to questions and answers!

1. Sawe and Kejelcha both broke 2-hours, but how much did Sawe beat the prior world record by?

The previous official men’s marathon world record was Kelvin Kiptum’s 2:00:35, set at the 2023 Chicago Marathon. Kiptum was about the same age as Sawe, and a strong favorite to be the first man to break 2:00 before very tragically dying in a car accident two years ago at age 25. Sawe’s 1:59:30 lowered Kiptum’s mark by 65 seconds. A ~1% decrease in the total race time, which is mind-blowing. This would correspond to a 0.1 second decrease in the 100-meter world record, which seems humanly impossible even with all the recent tech, nutrition, and training improvements.

Gone are the days of 5-10% improvements in the world record we saw in the 1900’s, though:

2. Was this an abnormally fast result given recent historical trends in men’s marathon?

Actually no! If you plot the last 2.5 decades of marathon world records, you actually see a pretty steady linear decline in the record time, and Sawe’s record creates no exception:

Notably, this linear trend isn’t going to last forever. I talked about it in the Power Law article, but most human-powered racing records eventually condense into a pretty clean logarithmic chart where the marginal record gains keep getting smaller as the absolute limit of human performance is reached. If you zoom back out to the marathon record curve over the last century above, you see this decaying improvement in action— it just happens to look more linear when you zoom in on one individual section with a lot of data points!

3. What recent sports tech and science advances fueled his accomplishment?

Sawe was effectively supported during the race by two companies: Adidas (for shoes + gear), and Maurten for nutrition. He mentioned in an interview that he had some toast, honey, and tea for breakfast too, but unfortunately we don’t know which brands he was representing for that meal.

On the gear front, Sawe raced in Adidas gear from head to toe. Up top, he wore a pretty standard singlet with some perforations to keep the fabric off his chest and back, presumably for cooling. However, the real hero of his kit was his pair of Adidas Adizero Adios Pro Evo 3— a brand new pair of super shoes that were released just days ago (and after the Boston Marathon, notably one of Adidas’ premier events). Adidas claims that the shoes have a best-in-class carbon plate and foam construction that maximizes tack height and energy return while minimizing weight. They weigh an absurdly light 97 grams total (~0.2 pounds).

The runner-up and second person to ever break 2:00, Kejelcha, AND women’s winner Tigst Assefa were also reported to be wearing the same model of shoe. In all, it was certainly a great day of validation for the Adidas team’s new product line and another feather in the cap of carbon-plated shoes and high-performance modern shoe foams for energy return. It will be fascinating to see what company achieves the next great leap forward in shoe components and what they will be.

On the nutrition side, the Maurten team has released a surprisingly detailed account of his fueling throughout the race and its lead-up on the day, which may eventually be removed for how much detail is divulges. I would imagine that this consistent-fueling plan is pretty standard for most athletes now, but it does stand out how light his breakfast was compared to how much actual race fuel he consumed close to the start. He also took some sodium bi-carbonate before the race, which in theory helps reduce the production of lactic acid in the bloodstream, but with generally unclear clinical results to-date.

Although he didn’t do anything super special nutrition-wise during the race compared to the modern standard, it’s unbelievable that athletes now consume ~100-120g of carbs per hour. It’s a step-change in the amount of energy input to an endurance athlete’s body compared to even 15 years ago.

4. Was there anything special about the weather conditions that powered the feat?

Nothing was as dramatic as, say, a ripping tailwind on the uni-directional Boston Marathon course, but the day did offer pretty ideal running conditions. When the elite runners started between 9:05-9:35, the temperature was ~50-55° F with a light breeze and some lifting clouds and humidity, which is pretty ideal as conditions go. By the time the elites finished, they were facing sunnier conditions around 60° F, which is also totally fine for a race of this duration. Not perfect weather for a world record per se, but well within the preferred end of conditions!

Given the conditions across all marathons all year, I’d say this was well on the right side of the bell curve for a record-setting event.

5. Did the women’s marathon record also get taken down this race?

Yes! With such solid conditions on the day and so much emerging tech and fueling being available to athletes nowadays, it’s perhaps not a surprise that Tigst Assefa broke her own world record with a 2:15:41 marathon time. The note worth adding here is that she set the “Women’s Only” world record for women, which means that the race group only had women to pace with. Ruth Chepng'etich of Kenya currently holds the “Mixed” world record of 2:09:56, which she set at the Chicago Marathon in 2024, becoming the first woman to break 2:10 in any format marathon. I think this distinction did muddy the waters a bit on Tigst’s new world record in London yesterday, but there’s no doubt that it deserves all the praise that can be heaped upon it!

6. Is there anything to be gleaned from this feat about the future of women’s marathon world records?

This is a tough question to answer, other than to not-so-boldly suggest that continued reduction in the men’s world record marathon should almost certainly correlate to a reduction in the women’s world record. However, I do think there are a couple interesting caveats.

First, our understanding of women’s physiology and athletic performance output is advancing more quickly than men’s right now, in part because we’re starting from so far behind. As we optimize training plans, nutrition, and even just actual running gear to better suit women runners, there could actually be an acceleration of the women’s world record that’s faster than the men’s. This optimization could be slowed by the lesser infrastructure for women runners compared to men, but I still think it can make a big difference in the rate at which the marathon record improves for each group.

On the flip side, the relative lack of elite women runners compared to men means that the chances of having an outlier performer that’s 5 standard deviations above the average pro runner are far lower. The number of total runners globally has boomed in the past three decades, and the heightened chances of finding a one-of-one or generationally elite runner have gone up significantly as a result and probably alone has contributed significant time improvements to the world record for men and women (which is strange to think about in practical terms). This drag on world record improvement is a pure numbers game, but it’s difficult to estimate its actual impact.

All-in-all, I find it difficult NOT to anticipate a pretty substantial decrease in the women’s world record over time (for women-only and mixed races) in the near future.

7. Should we be concerned about doping contributing to the world record progression?

I think there’s always a good reason to be skeptical in a healthy way about the results of any world record or generational performance set by an athlete (if the 80’s and 90’s in cycling were any indicator). However, for Sawe specifically, Adidas has poured tons of money into making him and other athletes as clean as possible, even recently re-upping their investment in his drug testing protocols for the next year. Of course, these tests are only as good as the performance enhancing drugs (PEDs) that they can screen for, but I think there’s enough reason beyond PEDs in the realms of technology, science, and weather that all could have yielded this result.

Overall, my concern about doping for this result is low, but not zero— maybe a 3 or 4 out of 10, which is my natural baseline. And on that cheery note…

There’s the bell!

Well, if we assume Betteridge’s “Law of Headlines” also applies to rhetorical questions used to tee up article topics, the answer should probably be “no,” and that turns out to be correct here! Going back five years since the lost pandemic season, we’ve had a total of 19 upsets in 2021, 21 in 2022, 19 in 2023, 19 in 2024, and a historic low of 11 in 2025. So far this year there have been 16 upsets with a possibility for 2 more in the Final Four. As it turns out, most of these numbers are well within the standard distribution of upsets for the tournament in the past 50 years, and close to the average of 18 upsets per tournament:

Despite the data providing negative, or at least inconclusive, evidence of a recent decline in March Madness upsets, I think fans have good reason to ask questions about the parity of their favorite tournament today with so much changing in the collegiate sports landscape. What perhaps previously lurked under the surface in terms of player payments and incentives to change rosters during college is now occurring out in the open. The transfer portal, the litany of court cases unlocking student name-image-likeness (NIL) rights, and direct athlete payments have made the most popular college sports more openly financial than ever. Schools can now even directly share up to $20.5 million annually with athletes under the recent House settlement framework. The same forces that drive performance disparity in many professional leagues may soon do the same to major college sports.

Which brings us to the not-so-obvious but very fun question as we sit at the front end of this new reality: how much NIL money is actually playing on the floor in this year’s men’s Final Four?

To be transparent from the get-go, the actual (annoying) answer is that nobody fully knows. College athletes earn money through an opaque mix of direct school revenue-sharing, collective-backed arrangements, booster-supported opportunities, personal merch, appearances, and brand campaign in a highly un-transparent manner (at least to the public). It’s gotten to the point where platforms like On3 have tried to make sense of the current landscape for each player independently to track player “NIL valuation” and team “roster value,” but those estimates are based on a subset of real concrete deal info, and a fair amount of extrapolation. Other sites like Opendorse, provide actual endorsement cost benchmarks and marketplace data, but don’t aggregate information about each player’s actual contracts. So any estimate here is exactly that: an estimate.

We have enough public information to make a strong educated guess about the total earnings of each Final Four team, but the lack of centralized tracking system means we probably have some wide error bars on the estimates.

According to On3, Michigan sits at an estimated roster NIL income value of $5.3 million, Arizona at $5.2 million, UConn at $3.2 million, and Illinois at $2.3 million. In total, On3 estimates that the teams combine to total about $16.0 million in roster NIL income, an average of $4.0 million per team. However, a recent Front Office Sports article estimated that university-based payments alone account for ~$4.3M/year at top men’s basketball schools (maybe even higher at UConn since they have a weak football program). If this latter fact is to be believed, then On3’s full team estimates are likely on the low-end of the likely total income range.

Additionally, On3’s own roster valuations are a bit tough to believe because certain known player valuations are so high. For Michigan, Morez Johnson and Yaxel Landeborg are each listed at $2.0 million, Illinois freshman Keaton Wagler is listed at $1.5 million, Arizona’s freshman star Koa Peat was cited by at $1.3 million when he committed, and UConn’s Alex Karaban was cited at $1.8 million in late 2024. Across these five players alone, ~$8.6M in NIL income is more concretely known. With an additional 43 players making up the team’s rosters, even with an exponential decay in NIL income, it’s likely that the total NIL income from all sources is maybe 1.5x-2x the estimate currently shown on On3’s site, especially now that these teams and players are in the Final Four of the tournament this year. To be somewhat conservative, I think it’s reasonable to assume that the collection of all players on the rosters of the Final Four teams will earn at least ~$24M in total this season— roughly $6 Million per squad.

Expanding the bracket to it’s original state pre-tournament, I think you could feasibly estimate that the total of NIL and university income for a team like UPenn or North Dakota State probably rounds to $0. Generously, the number might be in the $250k range if they’ve got 1-2 standout players that are good enough to transfer away next season. In effect, this means that the total “salary” of these higher—seeded teams that have made the Final Four is ~24x that of the lowest-seeded teams. It’s a pretty dramatic pay disparity that’s really only replicated in European soccer league structures that have major challenges with league parity and leverage relegation/promotion systems.

Even with all these crazy numbers being shared that would boggle the mind of a college athlete just a decade ago, I do think it’s fair to counter with arguments about deteriorating equality in college sports like:

• The top schools already attracted and retained talent at similar levels to what we’re witnessing, now we just have a real price tag we’re assigning to that power law of performance so it feels worse

• The bigger issue at play is the ability for students to freely transfer between schools to artificially elevate payments (which actual taxpayers pay for most of these schools, which is crazy)

• Coaches were already being paid in extremely disproportionate ways that affect recruiting and retention in a similar manner to player income

This final point is (I think) quite poignant, and is highlighted again by the coaches participating in the men’s Final Four this year. Dan Hurley’s six-year UConn deal is worth about $8.3 million annually, Tommy Lloyd’s guaranteed package this season is about $5.5 million from Arizona, Illini coach Brad Underwood makes $4.4 million per year, and Dusty May’s reworked Michigan deal reportedly puts him at $5.1 million this season. It turns out, it pays to be a good college basketball coach, with the Final Four coaches earning a collective $23.3M this year.

These extremely high coaching salaries have been around for a couple decades now, and it’s definitely safe to say that the top coaches earn between 10-50x what coaches from small schools pull in— a very similar ratio to the expected NIL salary ratios. If coaching salary corresponds to ability, which corresponds to results, then perhaps the new NIL payment totals going to players is merely a reflection of the existing coaches’ salary distribution as opposed to a new signal by which to judge parity and fairness.

How this all changes within the next few years is completely unknowable given how rapidly the college sports landscape is evolving. You can’t put the toothpaste back in the tube, so to speak, but at least we understand the baseline of income of players and coaches heading into this Final Four (and might be a little depressed about how much more money the college athletes are earning than we are— you’re welcome).

There’s the bell!

These, of course, are questions that I felt I was overdue to understand given how long I’ve spent using carbon-fiber products on the ice rink, tennis court, and while cycling. I also have some deep-seeded need to understand this material, how it’s made, transformed into products, and what may eventually replace it as a result of some subconscious mechanical engineering force. Unpacking that guilty need might be an article topic for another day though. For now, let’s jump into a (relatively long) summary of the history of carbon fiber, its mechanical properties, and its modern use cases in sports. Fair warning, this article got away from me a bit on length because it turns out carbon fiber is extremely interesting, so strap in!

As with most materials that become a core component of sports products ( and many innovations more broadly) carbon fiber was initially invented almost by accident without any initial use-case inside the world of sports. Perhaps unsurprisingly, the actual discovery of the material took place much longer ago than I had expected as well. I’m increasingly convinced that it’s generally safe to assume that most basic material innovations were discovered by an unknown scientist in the 18th or 19th century in pursuit of some completely far-fetched idea… In this case, it happened occur during the 19th century in attempt to stabilize an incandescent lightbulb!

As far back as 1860, British physicist Joseph Swan experimented with carbonized paper as filaments for incandescent lamps (a far cry from a tennis racket but we’ll get there). Swan heated organic fibers to extremely high temperatures >1000° C in the absence of oxygen when they would decompose and leave behind a thin filament composed primarily of carbon atoms that was capable of glowing when electricity passed through it. (side note: this is how the modern-day pyrolysis process works as a method for “locking in” embedded carbon in organic waste). The material could conduct electricity but provided enough resistance to heat up and generate visible light. In classic fashion, soon after this initial breakthrough by Swan, Thomas Edison (more famously) adopted a similar approach in 1879, carbonizing bamboo fibers to produce more durable incandescent light bulb filaments.

Even with Edison’s improvements, these early “carbon fibers” were fragile and inconsistent by modern standards, but they demonstrated something important: carbonized fibers could withstand very high temperatures, conduct electricity, and could be created in a fairly replicable manner. Due to their initial utilization in lightbulbs and best understood for their conductive properties, carbon fibers remained limited to electrical applications for many decades after their initial discovery. Interestingly, the next major breakthrough would not arrive for close to 100 years in the mid-twentieth century.

In 1958, carbonized fibers realized a transformation from electrical to mechanical applications when a a researcher named Roger Bacon (awesome name) at Union Carbide (cool company name) accidentally discovered that heating rayon fibers to extremely high temperatures — above 3,000°C in an inert, oxygen-free atmosphere — produced remarkably strong graphite whiskers. Some of these whiskers exhibited tensile strengths exceeding 2,000 Megapascals (“MPa”), far stronger than any previously known carbon fibers, and ~6x the tensile strength of popular modern aluminum alloys. In essence, these initial studies showed that the carbonized rayon fibers could withstand significantly more stress (force over area) than aluminum before the point where the material actually breaks— the aforementioned tensile strength.

Bacon’s work showed that carbon fibers could potentially become structural materials, but the major challenge was finding a way to manufacture them more efficiently and consistently.

As fortune would have it, that subsequent breakthrough came shortly after Bacon’s work across the globe in Japan! Dr. Akio Shindo at the Government Industrial Research Institute in Osaka discovered that fibers of the polymer polyacrylonitrile (PAN) could be converted into much stronger carbon fibers through controlled heat treatment in 1959. PAN represented one of the first synthetic fibers to undergo the same heat treatment as organic fibers like paper, bamboo, and rayon, and they proved far more suitable as a carbon fiber precursor material. They produced higher carbon yields, better molecular alignment (more on this momentarily), and significantly stronger final fibers, especially when taken to temperatures ~1,300+ C.

In short, the result of this first 100 years of research on carbonized fibers was the repeatable production of an extremely thin fiber composed primarily of carbon atoms— typically above 90% of the end product mass. Each fiber typically measures 5 to 10 micrometers in diameter, roughly one-tenth the thickness of a human hair. With enough production stability, thousands of these fibers could now get bundled together into larger strands called tows, which contain anywhere from 1,000 to 50,000 individual filaments, creating a strand up to 1.5cm in width (roughly 1/2 an inch). When we think of modern carbon fiber strands, we often visualize these tows as opposed to individual fibers that actually compose them!

At the most basic level, what makes these fibers extraordinary is the arrangement of their individual atoms. Carbon atoms in solid isolation naturally form strong hexagonal bonding structures similar to the crystalline layers found in graphite. However, in contrast with pencil graphite where the layers are oriented to intentionally come apart, carbon fiber graphitic layers become highly aligned along the axis of the fiber itself. This alignment means the bonds that carry load are oriented almost perfectly along the direction of the fiber itself, leading to extremely high atomic resistance to separation through bending or tensile fracture (covalent bonds for the win).

The result is a material with exceptional mechanical properties. Modern high-strength carbon fibers commonly achieve tensile strengths between 3,500 and 7,000 MPa. Most structural steel falls in the range of 400–1,000 MPa, and less dense titanium typically ranges between 900-1,800 MPa depending on the alloy. Even more impressive is the material’s stiffness (measured as Young’s modulus). In contrast to the tensile strength, stiffness is measured as the slope of the stress/strain— the linear slope in the chart a few paragraphs back! Standard carbon fibers have moduli around 230 Gigapascals (“GPa”), while high-modulus fibers can reach 500–700 GPa, making them significantly stiffer than steel around 200 GPa and titanium around 120GPa, which actually makes it more flexible than steel, somewhat counterintuitively.

However, carbon fiber’s most important advantage is not strength or stiffness alone; otherwise you’d probably see it in just as much sports equipment as steel and titanium today (i.e. not very much). Carbon fiber’s greatest asset is its strength-to-weight ratio. Most modern carbon fiber has a density of roughly 1.6 g/cm³, compared with ~7.8 g/cm³ for steel, ~4.5 g/cm³ for titanium, and even 2.7 g/cm³ for aluminum. This combination of low density, high stiffness, and extremely high strength produces one of the most impressive structural materials in widespread use today. If this sounds like it could be useful for a lot of sports equipment applications, you’d have a correct instinct!

Ok. Now that what we know what carbon fiber actually is (and how it was developed over time), it’s time to understand how it gets manufactured and converted from fibers and tows into actual products in the modern supply chain. Readers who are ready to end the more technical engineering section of the article can breathe a sigh of relief (me included).

The integration of carbon fiber into complex products, and the commercialization of its supply chain, accelerated rapidly during the 1970s and 1980s, driven largely by demand from aerospace applications. Aircraft manufacturers recognized that carbon fiber-based structures could reduce weight dramatically while maintaining the necessary strength and durability for flight. However, early carbon fiber products were generally extraordinarily expensive—costing more than $400 per pound in the 1960s. Scaling production and improving processes gradually reduced prices to the point where today, you can purchase raw carbon fiber for less than $20/pound, a roughly 95% reduction in cost!

Although the underlying processes for creating carbon fiber have evolved significantly over the decades, the basic production steps remain largely the same. Everything begins with the precursor polymer, usually PAN, which still makes up close to 90% of all carbon fiber products used today(!). PAN is produced by polymerizing acrylonitrile monomers into long molecular chains. This polymer is then dissolved into a viscous solution and extruded through tiny holes in a device called a spinneret, which functions somewhat like an industrial shower head. Each spinneret may contain thousands of holes, producing continuous fibers that are subsequently stretched, aligned, and treated as they solidify.

These precursor fibers then undergo a series of heat treatments that fundamentally transform their molecular structure. The first step is oxidative stabilization, a fancy term for heating the fibers in normal air to temperatures between 200 and 300°C. During this stage, chemical reactions including cyclization and oxidation convert the linear polymer chains into more stable ladder-like structures. Without this step, the fibers would likely melt or otherwise degrade during later heating stages. Interestingly, this is the only heating step that involves the presence of oxygen, with the operating temperatures remaining low enough to prevent combustion of the PAN fibers.

Next comes carbonization, the stage where the fibers actually have their carbon structure created and reduce the presence of non-carbon atoms. In large industrial furnaces, the stabilized fibers are heated in a nitrogen (or other inert gas) atmosphere to temperatures between 1,000 and 1,500°C. At these temperatures, non-carbon elements such as hydrogen, oxygen, and nitrogen are driven off as gases, leaving behind fibers composed primarily of carbon atoms. For certain high-performance fibers, a final stage called graphitization follows. During this step, the fibers are heated to temperatures as high as 2,500–3,000°C, further aligning the graphite crystals and increasing stiffness. By the end of this process, the fibers consist of roughly 90–95 percent carbon, with highly oriented crystalline structures responsible for their remarkable strength and stiffness.

Before the resulting carbon fibers can be used in composite materials (more on this later), they undergo surface treatments designed to improve adhesion with polymer resins down the road. Carbon itself is relatively chemically inert, so manufacturers use electrochemical oxidation or plasma treatments to introduce reactive surface groups that can help bundle the fibers into tows. A protective coating called sizing is then applied to protect the fibers and tows during handling and to further tailor them for assembly with resin. The finished fibers are combined into tows, which are wound onto spools and shipped to composite manufacturers, where they begin the next stage of their journey!

Converting raw carbon fiber and tows into finished sports equipment requires a completely different set of assembly and manufacturing processes than were used to create them. The tows are typically first be woven into planar patterns that resemble other fabrics, braided into tubes, or stitched into multi-directional reinforcement layers using an industrial loom— all different iterations of the bi-directional woven carbon fiber pattern that we associate with the material. This weaving pattern is intended to provide all the strength and stiffness characteristics we discussed across an entire plane or set of planes as opposed to a single direction for a single carbon fiber.

These woven carbon fibers themselves are rarely used alone in any application, let alone in a product that will be handled by a person. Instead, woven tows are typically embedded in a polymer matrix (usually an epoxy, which is just a thermo-setting polymer) to create a carbon fiber reinforced polymer (“CFRP”). Once an epoxy is introduced, the material becomes a composite by virtue of being comprised of multiple distinct materials. The term carbon composite is used quite regularly, and in these materials, carbon fibers handle nearly all of the tensile loads, while the resin matrix binds the fibers together to transfer forces between them and prevents fraying or separation over time. The resin can also protect the fibers from environmental damage like puncture or wear and helps provide the overall shape of the structure.

One of the most common intermediate composite materials is called “prepreg” (terrible name), short for pre-impregnated composite. Prepreg materials combine carbon fiber sheets with partially cured epoxy resin, which makes them shapeable/flexible, while still maintaining their overall structural integrity. Inserting a prepreg sheet of carbon fiber into a mold to create a specific shape is counterintuitively known as “dry” layup, while using a raw carbon fiber sheet and adding epoxy to it yourself is known as “wet” layup. Both forming methods have benefits and drawbacks, but if you happen to find yourself prototyping with carbon fiber, you’ll likely utilize a wet layup process because it offers more flexibility and often requires fewer thermal tools. In either method, you typically lay sheets over 3D molds of the final desired shape and either use temperature, pressure, and/or time to let the epoxy set into the desired shape.

Once this setting process is complete, there’s often a finishing step where you remove excess material, apply surface finish… and voila! You now have a completed carbon fiber component.

In many high-end sporting goods manufactured at scale, prepreg sheets are carefully layered into molds according to precisely engineered fiber layup schedules. Each layer is oriented in a specific direction—often 0°, ±45°, or 90° relative to the primary load direction—to create a composite structure capable of handling loads from multiple directions. Once the layers are placed in a mold, the part is sealed in a vacuum bag and cured in an autoclave, essentially a high-pressure oven. Typical curing conditions range from 120–180°C at pressures of 3–7 Bar, producing extremely dense composites with minimal internal voids.

Other advanced manufacturing methods include resin transfer molding (“RTM”) and filament winding, both of which enable precise control over composite structure and performance. In RTM, dry carbon fiber preforms are carefully arranged within a closed mold, after which a thermosetting resin—typically epoxy—is injected under pressure to fully impregnate the fibers before curing. This process improves fiber volume fraction, reduces void content, and allows for complex geometries with high surface quality. Filament winding, by contrast, involves continuously wrapping resin-impregnated fibers around a rotating mandrel in carefully controlled orientations. By varying winding angles (e.g., hoop vs. helical), engineers can optimize load paths for cylindrical or pressure-bearing structures such as bicycle rims, hockey stick shafts, and pressure vessels.

For all these assembly methods, the range of automation ranges broadly, with more complex shapes with external part interfaces (like bike frames) requiring far more manual touchpoints and molding. Although the underlying carbon composite constituent materials may be inexpensive, this actual product manufacturing and assembly process can serve as a major cost center, especially for more complex, lower-volume products. However, the overall wide range of assembly methods means that carbon fiber can be constructed into a larger variety of shapes than many other materials. With this in mind, it’s time to transition into the world of the actual carbon fiber sports equipment. It’s worth recalling for this section that carbon fiber is extremely strong, stiff, and lightweight, which makes it the perfect material for a LOT of different applications in the world of sports. I’ll list a handful below, but there are hundreds more worth exploring.

Carbon fiber began appearing in sporting goods in the late 1970s and early 1980s, initially in high-end, performance-focused applications such as racing bicycles and tennis rackets. Early adopters were willing to pay a premium for marginal gains, and carbon fiber already offered more than a “marginal” gain: it represented a step-change improvement in stiffness-to-weight ratio compared to aluminum or steel in many products. Of course, early composite components were often handmade and inconsistent, but as manufacturing processes such as prepreg layup and autoclave curing matured through the 1980s and 1990s, reliability and repeatability improved. Once production methods caught up— particularly with better control over fiber alignment, resin systems, and curing cycles— carbon fiber rapidly transitioned from a niche material to a dominant one. The fundamental appeal remained consistent: engineers could design structures that were simultaneously stiff, lightweight, and selectively flexible in ways that traditional isotropic materials simply could not match.

In cycling, carbon fiber enabled a complete rethinking of frame design. Early carbon bikes in the 1980s often used bonded lug-and-tube construction, but by the 1990s and 2000s, manufacturers began shifting toward monocoque frames—single, continuous composite structures molded as unified shells. This allowed engineers to optimize aerodynamics, eliminate joints, and fine-tune stiffness in specific regions such as the bottom bracket and head tube. Today’s elite road frames routinely weigh under 2 pounds (insane) while maintaining exceptional torsional rigidity and impact resistance. The design philosophy mirrors developments in industries like Formula 1, where carbon fiber “monocoques” have long been used to combine minimal weight with extreme strength and crash protection. In both cases, the material enables not just lighter designs, but entirely new structural architectures. It’s a bit outdated, but this LOOK bike factory tour gives a great overview of how carbon fiber bike frames are still created and assembled today:

In tennis, the transition from wood to composite rackets began in earnest in the late 1970s and accelerated through the 1980s, with graphite (a form of carbon fiber composite) quickly becoming the dominant material for racket frames. Wooden rackets, which had been standard for decades, were limited by their relatively low stiffness and small head sizes. Carbon fiber composites allowed for significantly larger racket heads—improving the “sweet spot”—while simultaneously reducing mass and increasing stiffness. Modern rackets typically weigh between 280 and 320 grams and can be engineered with highly specific flex profiles to balance power, control, and vibration damping. This shift fundamentally changed the sport, enabling faster serves, heavier topspin, and a more aggressive baseline style of play.

Rowing equipment and hockey sticks offer yet two more parallel examples of how carbon fiber reshaped performance by replacing traditional wood-based designs. For most of the 20th century, racing shells and oar shafts were both built from wood—typically cedar or mahogany—requiring careful craftsmanship but limiting stiffness and weight reduction. Beginning in the 1980s and accelerating through the 1990s, carbon fiber composites enabled the transition to fully molded monocoque shells, dramatically reducing weight while increasing hull stiffness and dimensional consistency. Modern elite shells weigh as little as ~14 kg for a single (including riggers and ballast), yet must withstand significant cyclic loading from rowers and water impacts while maintaining precise hydrodynamic shapes.

Hockey sticks followed a similar trajectory: originally carved from solid wood and later constructed from laminated wood, they were durable but heavy and relatively inconsistent. The introduction of carbon fiber composite sticks in the late 1990s and early 2000s allowed manufacturers to engineer flex profiles, kick points, and blade stiffness with far greater precision. Today’s top-end sticks can weigh well under a pound and are designed to store and release energy during shots, increasing puck velocity while improving control. For both crew shells and hockey sticks, carbon fiber did more than reduce weight—it enabled a shift from handcrafted variability to highly engineered, repeatable performance, fundamentally changing how equipment is designed and how the sports themselves are played.

Golf provides another clear example of carbon fiber’s impact on performance. Traditionally, golf shafts were made from steel, offering durability but limited “tunability” and relatively high weight. Beginning in the late 20th century and accelerating into the 2000s, carbon fiber shafts became increasingly common, particularly in longer clubs like drivers and fairway woods. These shafts can weigh as half as much as their steel counterparts while allowing engineers to precisely tailor flex profiles, torque, and kick points to match different swing speeds and player preferences. Although this level of customization can enable golfers to optimize energy transfer during the swing and increase club-head speed in some cases, steel golf shafts are still very popular for irons and putters, which tend to have heavier clubheads than woods and often feel top-heavy or unbalanced with a super light carbon fiber shaft. (Maybe it’s not the best material for instances where you have to balance other heavy components in a piece of equipment)

Perhaps most notably in recent years, carbon fiber has played a central role in the evolution of elite running shoes. Modern “super shoes” incorporate thin carbon plates embedded within a two-part midsole. These plates act as stiffening elements that alter the bending stiffness of the shoe, effectively creating a lever-like system that improves energy return significantly compared to non-plated shoes. When combined with advanced foams (such as expanded thermoplastic polyurethanes or PEBA-based materials) the result is a system that stores and releases mechanical energy more efficiently than traditional running shoes. Controlled studies have shown improvements in running economy on the order of ~4-6 percent, a margin large enough to meaningfully impact elite competition outcomes. This has led to widespread adoption at the highest levels of the sport (as well as regulatory scrutiny from governing bodies seeking to maintain fairness), and as the shoes become manufactured at scale, they continue to become more accessible to non-pros.

Despite its remarkable properties, carbon fiber is not without its drawbacks. One of the most significant challenges is its tendency toward brittle, fracture-based failure mechanisms. Unlike metals, which typically exhibit ductile behavior—bending or yielding before fracture—carbon fiber composites can fail abruptly once their strength limits are exceeded or they undergo stress perpendicular to their fiber alginment. This lack of visible warning can pose risks in both sporting and structural applications, particularly under unexpected loading conditions. A perfect example of this failure mode is a composite hockey stick snapping during a slapshot that catches too much ice (even if an old wooden one would have broken at lower forces from the player).

Carbon fiber structures are also susceptible to delamination over a prolonged lifespan, a failure mode in which individual layers of the composite separate due to impact, fatigue, or manufacturing defects. Because composites are built up from multiple laminated plies, their integrity depends on the strength of the interfaces between layers. Delamination can significantly reduce load-carrying capacity and may not always be visible from the surface, necessitating advanced inspection techniques such as ultrasound or thermography in high-performance applications. It’s worth noting that both of these failure modes really only happen under extreme loading conditions and prolonged use, so the odds of a running shoe plate snapping are practically zero, but a bike frame or Formula 1 car body can dramatically fail when undergoing dramatic loads (e.g. during crashes).

Additionally, carbon fiber composites are inherently anisotropic, meaning their mechanical properties vary depending on fiber orientation. While this allows engineers to tailor performance with extraordinary precision, it also introduces complexity. Designers must carefully plan fiber layups to ensure that structures can withstand multidirectional loads, including tension, compression, shear, and torsion. Poorly designed layups can lead to unexpected weaknesses, even if the material itself is exceptionally strong along certain directions.

Even so, the advantages of carbon fiber have proven overwhelming across a wide range of applications, particularly in sports where marginal gains translate directly into competitive advantage.

Today, the global carbon fiber industry is dominated by a relatively small group of major manufacturers, including Toray Industries, Mitsubishi Chemical, Teijin (Toho Tenax), and Hexcel. These companies produce not only the raw carbon fibers but some also produce the intermediate materials such as prepregs and woven fabrics that are used by downstream manufacturers to create finished sporting goods. The supply chain is highly specialized, with tight control over precursor materials, processing conditions, and quality assurance. Although there is a slightly more competitive landscape, it’s very similar to microchip manufacturing in some ways with a few dominant players that have process and tooling advantages.

Looking ahead, carbon fiber is likely to expand into even more areas of sports technology, particularly where weight savings and energy efficiency are critical. Emerging applications include smart composites with embedded sensors for real-time performance monitoring, hybrid materials that combine carbon fiber with other reinforcements such as glass or aramid fibers, and structures that integrate electrical functionality. While carbon fiber itself is electrically conductive, its conductivity is anisotropic and relatively low compared to metals. Although it’s not a focus of this article, it can still be leveraged for applications such as strain sensing, electromagnetic shielding, or even integrated heating elements.

At the same time, researchers and manufacturers are actively working to address one of carbon fiber’s biggest limitations: sustainability. Traditional carbon fiber production is energy-intensive and relies heavily on polyacrylonitrile (PAN) precursors derived from petrochemicals. New approaches include alternative precursors such as lignin (a plant-based material), improved recycling processes, and thermoplastic composites that can be more easily reprocessed. Recycled carbon fibers, while typically shorter and less aligned than virgin fibers, can retain a significant percent of their original mechanical properties, making them viable for many secondary and low-force applications where weight and stiffness are still a priority.

Several modern material systems are challenging the dominance of carbon fiber’s ubiquity— not by matching it outright, but by outperforming it in specific dimensions. Glass fiber (better known as “fiberglass”)remains the most significant competitor in cost-sensitive applications, offering far lower material and manufacturing costs while delivering sufficient strength for industries like automotive, marine, wind energy, and infrastructure. Basalt fiber has emerged as a potential middle ground, combining improved corrosion resistance and thermal stability with moderate cost. In contrast, aramid fibers (e.g., Kevlar) and ultra-high-molecular-weight polyethylene (UHMWPE) excel in toughness and impact resistance, making them superior in applications such as armor, helmets, and ropes where carbon fiber’s brittle failure is a disadvantage. Because no single alternative matches carbon fiber across all metrics, many industries increasingly rely on hybrid composites—blending carbon with glass, basalt, or aramid—to balance stiffness, cost, and durability.

A second class of challengers targets carbon fiber’s limitations in temperature performance, manufacturability, and material behavior. Ceramic matrix composites (CMCs), particularly silicon carbide systems, can withstand temperatures approaching ~1500° C, far beyond the limits of polymer-based carbon composites, making them essential in aerospace propulsion and thermal protection systems. Meanwhile, advanced lightweight metals such as aluminum-lithium alloys and additively manufactured titanium continue to compete in applications that require isotropic properties, easier inspection, or repairability. At the highest level though, rather than being replaced outright, carbon fiber is more likely to remain relatively dominant while beginning to share the landscape with complementary materials optimized for specific performance niches.

Despite growing competition from alternative materials, carbon fiber continues to become more affordable and more widely adopted as manufacturing techniques improve. Advances such as automation, out-of-autoclave curing, and faster production cycle times have steadily reduced costs, allowing carbon fiber to move beyond elite, high-performance applications into mass-market products—from bicycles and tennis rackets to protective gear and even everyday footwear. It is, in many ways, a remarkable trajectory: a material originally developed through experiments in high-temperature light generation has evolved into something so scalable and versatile that it now appears in mass-produced shoes and sporting goods. Rather than being displaced, carbon fiber is not only holding its ground but expanding its footprint—cementing its role as one of the defining materials of modern engineering and sport.

And when the next carbon fiber trainer, or carbon composite piece of golf equipment hits the markets you can also now tell your co-workers, “I actually read a newsletter post about carbon fiber a couple weeks ago! I don’t remember all the details about it, but it had something to do with a lightbulb.”

In order to honor the success of the Winter Olympics in Milan-Cortina and to load some of these stories into my long-term memory more effectively, I compiled below what I think are some of the most salient stories and events from the event (as I did for the Paris 2024 Olympics). A lot happened, so I’ve tried to keep my synopses brief and link to longer-form articles and videos. My own exposure to the coverage definitely skewed more towards US-based storylines, but I hope this summary of the event gives everyone some useful high-level water cooler or Zoom call fodder for the next few weeks!

1. The Canadian men’s curling team embroiled themselves in scandal by double-touching their stones during an early match against Sweden. This had to be the first inclusion on the list. This story permeated far and wide early in the Olympics and generated an incredible quote from the Canadian coach, who criticized the officiating of the event and said “we’re not at some bon-spiel in Saskatchewan just trying things out, we’re at the Olympics.” Honestly, despite the scandal being pretty ridiculous and spawning some incredible memes, it added a lot of drama to the match with Sweden and brought a lot of buzz to curling as a whole!

   

2. Norwegian cross country (Nordic) skier Johannes Klæbo literally ran away from the competition and swept the available events he competed in, winning six gold medals in six races. He set the record for most gold medals in one Winter Olympics (Phelps had eight golds 2008), and won both a 1.6km and a 50km race, which is a mind-blowing accomplishment in the world of endurance sports. His uphill sprinting technique also generated a ton of memes that I recommend you peruse. Below is one from the French Embassy in the US:

   

3. Things weren’t as peachy on the Norway Biathlon team, where one of their bronze medalists admitted (unprompted) to cheating on his girlfriend during a post-race interview. It was a bizarre admission and situation overall that I can’t imagine helped their relationship, but maybe I’m wrong (I doubt I am).

4. Italian speed skaters won the mixed short track relay event, and the anchor leg spun around at the finish line to celebrate in the face of his opponents. The skater later apologized, but I absolutely love the idea of athletes spinning around to finish a lot of races backwards in the face of their opponents. It would certainly spice up most events.

   

5. In her final Olympics, Lindsey Vonn suffered a brutal crash that required significant surgical repair on her lower leg. A lot of think pieces were penned about her decision to ski on a ruptured ACL, but at the end of the day, I think it’s clear nothing could stop her from giving it one last go. With Mikaela Shiffrin winning what might be one of her final Olympic medals as well, it really feels like we’re closing the book on an incredible era for skiing in the US.

6. The “Quad God” Ilia Malinin led the US team to a gold medal in the team event, but unfortunately fell apart in his final individual skate. On the flip side, American Alysa Liu completed an incredible comeback to figure skating and won gold in the individual event, complete with an incredible final skate. Side note: I just found out that the backflip Ilia does is worth literally zero points in the actual scoring? He just does it for the love of the game I guess.

   

7. American Elana Meyers Taylor became the oldest Olympic champion in history by winning the “mono-bob” event at 41 years old. I see no reason she couldn’t come back to win again in 2030, or maybe beyond that!

8. A wolf dog aptly named “Nazgul” invaded the cross country skiing finish line to celebrate with skiers, and got some great air time. Honestly, if I saw this dog running after me, I’d probably freak out unless I knew wolves weren’t present in the region around the venue.

   

9. In one of the best-produced segments on the NBC broadcast, Olympic legend Shaun White threw down an emotional halfpipe run on the Olympic course. I miss watching the flying tomato for sure, but I think he’s a great addition to the overall broadcast at the games.

10. Lucas Pinheiro Braathen won the Alpine Skiing Giant Slalom and took home Brazil’s first eve winter olympics gold medal. It’s not often that any county wins their first gold medal in the Winter Games, so it’s incredible to watch unfold when it does!

11. Norwegian Alpine skier Atle McGrath missed a gate during the finals of the team slalom race, and reacted as we all would. He threw his poles into the woods, took off his skis in the middle of the hill and walked to the edge of the ski run to just lay down by himself. Unfortunately, it spawned some incredible images and footage.

12. French biathlete Julia Simon decided to hush the haters as she crossed the finish line to win gold in the 15km individual event. For better or worse, her signal was directed at those who had reported on her convicted acts of credit card fraud and theft from one of her old teammates. So maybe some respect is due to the haters… but in any case this caused quite a stir.

13. This didn’t get a ton of press during the games, but with some inclement weather and questionable hill conditions, over fifty slalom skiers DNF’d their first run at the Olympics. Honestly, it’s kind of puzzling that the organizers let this happen without restarting the event, but maybe the schedule was too crammed to permit re-runs.

14. The new Dual Moguls event yielded one of the craziest videos I’ve ever seen of skiers. I honestly don’t know how either of these guys were able to stay upright — I would have exploded into a thousand pieces if I had to manage either of their “deviations.”

15. Both the US Men’s and Women’s hockey teams took home gold over Canada in some of the most incredible hockey games I’ve ever watched. Despite the controversy surrounding the men’s team after the game (and let’s be a bit honest in saying before the game as well), I still find it hilarious that the US goaltender Connor Hellebuyck will be awarded a Presidential Medal of Freedom for his performance. I guess it is difficult to watch his unbelievable highlights from the gold medal game and not think to yourself “this embodied the spirit of endurance, excellence, achievement, and most of all freedom.”

16. Finally, I have to give a shoutout to a couple on Reddit that replicated Winter Olympic sports in their home with whatever they had lying around. Part One and Part Two of their endeavor are both a joy to skim through and honestly provide some great halloween consume ideas for those in need this year.

I honestly cannot wait for the 2028 Summer Games in LA and am even more excited for 2030 in the French Alps. I think this Summer-Winter cycle with Paris and Milan really got me back in on the Olympic action.

There’s the bell!

The amount of individual and collective effort and creativity that goes into launching any new company is difficult to overstate, so the fact that the team at RLS managed to launch such a polished product in the midst of rapid growth is a testament to the entire team’s work. Solving difficult product design challenges is extremely fun and rewarding, but the development process can be extremely nonlinear and it’s often difficult to actually productize a new invention. It’s equally difficult to commercialize a new product and to build a company around it — doubly so when you are creating the product and commercializing it simultaneously.

With my own excitement for RLS as a product and admiration for the RLS team, I jumped at the chance to interview some of the brains and brawn behind it: CEO Jamie Cook, and Head of Research Maria Kaimaki. The discussion with Jamie and Maria helped close many gaps in my own knowledge about how bike helmets work, how they are tested, and how the team that developed RLS managed to do it. More importantly, I think their answers reveal their depth of knowledge and passion for these topics, and provide glimpses into the future of not only bike helmet technology, but the likely evolution of helmets in other sports and industries too. I’m excited to watch it all unfold!

I am very thankful for Jamie and Maria’s time, and for Mathilde managing our time-zone-scattered schedules to coordinate the call! This inaugural Geek Locker interview is relatively long, so I’d definitely recommend grabbing a cup of coffee or tea to settle in, or listening through the narration feature if you’re on the move. If you’d like to see more long-form interviews like this in the future, please don’t hesitate to reach out!

Cam: To jump right in, I think the first question that I had was: it seems like your technology is more broadly applicable outside the world of just cycling. Potentially in MotoGP and other motor sports, but then also in ice hockey, potentially American football, and many more. I’m curious, you know, beyond your own familiarity with the sport and its products, what made you focus on cycling as your beachhead for this new technology?

Jamie: So, RLS came from the company HEXR [a 3D-printed bike helmet company]. And HEXR was established because we, at the time, saw 3D printing as a major opportunity to reshape and redefine how energy absorbing structures could be created.

At the time, I was doing a lot of rowing, and used the bike to get around London because it was too expensive to get the tube. So I would spend, I don’t know, two to three hours cycling around London most days. And if you’ve ever cycled around London, you’ll be pretty familiar with just the amount of near misses. And unfortunately, I witnessed some pretty bad accidents that then led me to going to a bike shop and try to understand what elements of protective gear were actually safe and how helmets work. I realized that most helmets really were just the same, and there hadn’t been significant innovation for many decades.

From that interest, I began to research 3D printing structures to replace the energy absorbing foam used in bicycle helmets. Inadvertently, we found that the outer shell [of the helmet] would separate on impact, and while it wasn’t particularly effective, it was a new approach for thinking about how helmets reduce harmful forces, and that started the R&D evolution towards the Release Layer System.

Maria: If I can add something to this, I think an interesting point is also that when you’re creating a new technology, you want to test it in a way that your target audience would understand its potential. And the cycling helmet market is a very good one to start from because of how visible the accidents are, and because of how it’s been a pioneer for changes in safety standards. The way many helmets are tested to date is against a flat surface to measure their linear energy absorption. However, we know that that is not sufficient, rotation in the brain is what is responsible for injuries such as concussions.

In the European standards, it is the cycling community that has came together to come up with a new method of helmet testing to push for more rotation mitigation. So using the cycling helmet as an example implementation was an important one for us to show how our rotation mitigation technology responds to upcoming changes in standards and new learnings from research by the injury biomechanics community.

Jamie: Also, Cameron, is it helpful just to give you a bit of background to our roles and, you know, what we have done in terms of experiences in engineering?

Cam: Yeah, that would be great, certainly. I’ve got a little bit just from the background on the company and also your LinkedIns, but yeah, absolutely happy to get some more detail there.

Jamie: Yeah, well, I guess I can go first. So my background is in Mechanical Engineering — I did my undergraduate for 3 years at UCL, in London and then postgrad in engineering science at Oxford University. As I left Oxford, I spun out HEXR to commercialize a 3D printed helmet.

So really from 2018, I’ve been more of a jack of all trades, master of none compared to being, as Maria is, deep in the trenches on R&D and science. But the team here are really focused on innovation. So while we’re still quite a small team around 15 people, most of us have deep experience around engineering or design.

Maria: My background is originally general engineering from Cambridge with a focus on control, so how all the individual materials and components come together to allow for the desired function of a system. Then I followed the academic route for quite a while, so I did my PhD and postdoc, exploring different aspects of this. In my PhD, I researched how materials interactions at the very small scale can affect the function of an engineering system often in an irreversible manner. Then in my postdoc, I moved towards biomechanics and investigated how biological systems work and adapt to match their needs.

So how do we tune a system within the constraints of biomechanics? Then I was interested in seeing how this knowledge can be applied in real life and into a product, which is where I diverged from the academic path and joined HEXR about 3 years ago.

Cam: Awesome. No, this is sparking many other questions that I think I would have for you personally outside of the Release Layer System work! A related question I have, which is more (I think) both materials and manufacturing focused is: I would assume that the majority of your panel components now are injection molded, especially as you look into incorporating your technology with 3rd party helmet vendors in the market. If you made the shift, I’m curious how you transitioned from 3D printing to injection molding, and if you saw any challenges in the process moving from prototyping the system more generally into actual mass manufacturing and scaling of the product.

Jamie: Absolutely. I was really excited about 3D printing because it provides complete freedom in design. There’s no limitation that you get with maybe injection molding and thermoforming where you need a tool set first, and often it’s quite expensive to begin making products. But with 3D printing, unfortunately, the materials are generally less consistent — at least this is going back five to seven years ago now. So while there was this freedom to design, there wasn’t necessarily the same sophistication around the quality and diversity of materials.

These deep experiences with 3D printing have given us unique DNA for how we think about design and product in the future. So we try to do things differently when it comes to injection molding and thermoforming: we try to take what we’ve learned with 3D printing (and Maria can go into this in more detail) to help us think about this open-ended question of how do we best protect people and prevent concussions?

Maria: I would say you might expect that a lot of our innovation would be focused mainly on the product, but actually we found that innovation was also needed on the process: how do we make something like RLS initially, then what do we need to change to make itin a repeatable way at scale. We had to think of all the assumptions and properties that might change when RLS is created at scale to make sure we are testing and breaking our own product appropriately to ensure that it stays robust as it can be as we transition out of prototyping. So it has been quite challenging, but I think it’s been a very interesting path.

Cam: Awesome! And I think related to that, I’m curious if you guys found any specific processes or tools that you found particularly useful in the transition from prototyping to scaled manufacturing?

Jamie: Well, going back to the early days, when we were first prototyping this ball bearing concept, we actually made the bearings via 3D printing, which was an unbelievably expensive process. And the quality wasn’t always that consistent, but it allowed us to again be incredibly quick and open-ended with how we thought about tackling the problem. And often, you know, you can go down in this route and things do stick and you learn about, you know, geometry and material properties far quicker by having lots of different ways of experimenting. So I would certainly say using 3D printing has been incredibly effective to help us quickly understand things.

Maria: One interesting thing is that if you come to our lab you might be surprised with the kind of equipment that we have because it’s not only mechanical testing equipment, as people would expect. It’s also equipment to test a materials’ viscosity, or the properties it has at different temperatures, similarly to a wet chemistry lab, which is not what people imagine when you say that we are doing innovation on a helmet.

So I think those tools that allow us to fully characterize a system that has moving parts have been very helpful for us. And together with the fact that the people who work in the company have varied expertise, this means that every time we’ve asked why something happened in production, we could quickly either prototype a tool or buy a tool to actually replicate and test that effect and see whether it might matter for the product down the line.

Another thing that I find has been very useful is that we have a state-of-the-art helmet testing facility in house, an early investment from the company. That has meant that we can not only come up with interesting new developments, but we were able to quickly answer the question: doesour idea actually make a difference? And that has massively helped in that feedback loop, right? We’ve got all the information straight away: are the components what we want them to be, do they match their predicted properties and does the final product work?

Cam: Awesome. I guess to take a step back and look at the product as a whole, were there any other concepts aside from the ball bearing design that kind of got left on the cutting room floor, so to speak?

Jamie: Top secret.

Cam: Haha that’s totally fine.

Jamie: I mean, there’s certainly a lot of interesting things. Whether or not they fully work, you know, it’s sometimes hard to know. But I think innovation can feel like a jigsaw puzzle, there are all of these different variables and interdependencies. So, you know, we would obviously rush to try and get a solution to market and find something that works, but often when you’re pioneering, there’s a whole sea of other interesting ideas. And we’re still trying to just catch up, to be honest, because there’s a lot of commercial interest for the technology right now.

But certainly, I would say that the future is bright in terms of what we can do to bring more safety to more people.

Maria: And something that has been very important for us is figuring out how to capture those ideas. Because we’re moving fast, we’re testing a series of concepts and we’re moving forward with specific options, we’ve had to go back and forth as we’ve gained many insights from the different ideas that we’ve ideated and tested. So early on, we realized it’s really important that we capture all our ideas so that they can also come back and complete that jigsaw puzzle that Jamie was describing.

Cam: Awesome! Yeah, I think a related question in terms of scaling the company and looking to commercialize at the same time is: how did you both think about structuring your technical teams and then also the company as a whole while you were growing from one to three to now fifteen people and simultaneously wanting to keep development velocity and also look at beginning to sell the product and engage customers.

Jamie: So, really good question. And I would say we’re actually quite lucky because the business model that we’re following is an ingredient business model. A big part of our value is to constantly create the best technical solutions for our brand partners. Then , our brand partners are largely responsible for marketing and selling the product with our technology to consumers. So that means we are able to have a disproportionally heavy R&D capability, and that’s valuable for the industry, and it’s valuable commercially for us as well.

In many other product companies, you might have an R&D team, but most likely there is a much larger sales and marketing team. And what we’re finding is that there’s a lot of organic demand for our technology, which is allowing us to still be very focused on just developing best in class product solutions.

I guess what we’ve done very well over the last 5 years is build out our capabilities specifically for developing solutions. It’s production (which in itself is very innovative). As Maria’s alluded to with testing facilities, capabilities surrounding how we record data, and there’s product development, which has a slightly different nuance to R&D. There’s all these different facets, but they all come together to create a great solution for our brand partners, and we anticipate that these solutions will only become cheaper and even more effective in the near future.

Cam: Awesome. And then maybe a similar line, in terms of growing the company from here, do you think about growing the team based on achieving milestones? Especially as you go to partner with more and more companies to actually sell your product, are you looking for increased demand signals to grow the team or are you more proactively looking to research and develop in different areas to kind of drive more of a supply sided growth for the company?

Jamie: Yeah, I would say strongly we’re trying to grow the team, because we know that there is going to be more and more interest for the technology. So at the moment, we believe there’s good applications for the technology in sports, cycling, equestrian, snow, and there’s also motorcycling (another big market). And then we’re pretty hopeful about industrial applications too.

Yes we are scaling the team, and we’re trying to get ahead of the curve. It’s always quite difficult to know exactly what that curve looks like because there’s also just day-to-day things to deal with in terms of quality, production in Asia, and so-on. So how we juggle our resources is always quite challenging— it’s a constantly moving target. I think the next three to six months are going to be tough just to deal with what’s right in front of us, but I’m hopeful over the course of 2026 we’ll have more and more facilities and team members to continue to evolve what we’ve built.

And even just head protection is one application! Who knows where this type of concept could go in the future. And that’s not just deflecting type solutions, it could be things that are completely out of the box, and things that we’re already interested in.

Cam: Fantastic. Well yeah, I think another question as more deep dive on the actual development side, were there any specific technical challenges that you guys were surprised about the difficulty? So, could be something extremely small in terms like material selection that was extremely difficult or a design aspect that was particularly challenging.

Maria: Well, whenever you’re putting together a series of components, it’s always a challenge. You characterize each of the components separately, and you know what you expect. You often think “okay, the system is the sum of its parts,” and then you put it together and it does something completely different in the beginning. So we constantly had to move between a system-level and a component-level approach, which was also then how the team was built and the expertise had to be varied to bring it all together.

Then, the fact that the technology is on the outside of the helmet meant that, in order to be proactive about questions that we thought we would get we had to develop a whole new set of testing protocols that we didn’t have any references for. You know, what does it mean for a helmet to be durable? People are thinking of damage due to the accidents that they are seeing, but what about regular wear and tear issues? It was very interesting to try and relate the work back to real life, and test the helmet across a host of very extreme conditions to answer potential questions.

But that’s kind of what I would say: we always try to also shift our position from somebody who’s developing a product to somebody who’s using a product. Saying “what would make me not buy this?” Then we started adding in the aesthetic aspects of it, the manufacturing aspects of it, all of that.

Jamie: Yeah, and when we first started, we actually tried to manufacture everything in one hit to reduce costs but it became very complex and introduced new technical challenges.

An important trait during the journey of innovation is how one has to constantly change their perspective. If you think “this is the way you should do it,” 9 times out of 10, it’s going to be wrong. You need to constantly remind yourself to open your eyes and mind to new ways of approaching the same problem. So it could be, for example: okay, let’s find one adhesive that solves it all, and then you open your mind and say, oh, we can actually use more than one adhesive to try to tackle all these various problems like durability or performance simultaneously. And later, “oh, actually, maybe we don’t have to look at adhesives at all! Let’s look at something entirely different, like a mechanical solution.” So it’s constantly about being flexible and open-minded to see the same problem, but from different perspectives.

Cam: Yeah, this is, again, making me think of probably 25 additional questions that I could ask! Obviously, I think more component level innovations, like the Release Layer System, require a lot of strategic IP (intellectual property) around them, and also probably for you guys, a rapid implementation of that IP, since you’re also looking to grow the company really quickly. So I’m curious how much effort went into your strategizing for IP before you actually went to implement it? And then, also any big takeaways from that process: was it more challenging than you expected? Less challenging? More or less of a time suck?

Jamie: Yeah, I can definitely answer this one.

It’s been a five-year IP strategy play. So luckily, some of our core patents have now been granted internationally, which is super important if you’re licensing a technology like we’re doing. The timeline on those — they can easily take three to six years, or even beyond that, so it was really gratifying to see that this year those core patents were granted.

Then obviously, the helmet industry has been going for a long time, so there’s a huge amount of IP and patents that are already in this space. So we’ve had to just be very mindful of navigating a course with the technology that allows us to have a clear freedom to operate, and that’s even more important when you think about how valuable the space is and how big some of the players are who are already operating in the space.

Maria: If I can add something, I think it’s quite interesting how you think about IP from an R&D perspective. When I joined, Jamie said something that stuck with me, which was, “whenever you’re struggling to solve a problem without an obvious solution, and you come up with a way of solving it, just note it down because that’s probably newand it is something that may be worth some drafting of IP”. Early on we found that we were just working to finalisedthe product, but you can’t do the work now and think of the IP later. So now all of us, as we’re problem solving, we’re also auditing what that means for our patents and our ideas and where they could grow. And that continuous process is something that’s really important.

Jamie: Yeah, it’s definitely fundamental, actually, with business models to be generating IP. And it’s an incredible valuable thing if you’ve got a really powerful patent because it’s essentially 20 years of exclusivity.

Cam: Awesome. Well, I think I just have 2 questions left at this stage. First, I’m just curious as you guys went through (probably) many late nights of sprinting to get demos done for potential customers or investors if there are any stories heroics that stood out to you in the multi-year process of getting this product to market that you wanted to highlight!

Jamie: Definitely one that comes to mind!

Maria: I have a few too! Go ahead.

Jamie: Well going back a few years now we developed what we thought was a good solution using a newly developed connector. And then we found that the connector would basically break at extremely high and low temperatures, and the whole thing could have become a critical issue with certifications.

At the time, we were also fundraising so it felt like a real pinch moment for the business as a whole. We sat down as a team and talked about it and we made an action plan to solve the problem. We got everybody involved on the problem. And there was this incredible drive and grit to figure out how to solve it.

Now 2 years later, we’re in a position where we’ve got a product that is working, and yeah, it’s not always certain how things will go, but I was really impressed and proud of the team.

Maria: Let me give you some more insight on the process of solving that problem as this was one of my moments too!

Me and a colleague were in a walk-in food freezer, in -10 degrees all geared up trying to do some low-temperature testing and we had conflicting feelings. We were very cold, so we wanted the connector to fail so that we could get out as soon as possible. But we also had worked so hard to make it work, so at the same time we wanted to be in there as long as possible because that would mean it’s working. It ended up working so we had to take turns to warm ourselves up and doing that testing. So there’s a lot of funny stories from those moments.

Jamie: He’s not still in the freezer is he?

Maria: Haha no, no.

Cam: That’s incredible. Yeah, I think last question on my end is: as you think about the company with both the external-facing side, but also the internal R&D/management side, what is next for you and what gets you most excited about the next 6 to 12 months?

Maria: For me, one of the most fun things was that for a while I would tell people what I do, but they didn’t really understand or believe it. And this year not only did we launch a product, but we also did a lot of testing externally for people to understand what RLS is and how it performs without us being connected to the testing. We then put together an academic publication where we addressed some of the gaps in knowledge from the academic community and some of the changes that are coming from the standardsto tie in together with the consumer and commercial side of things.

Now, we’re also communicating with all these different types of stakeholders and we’re connecting all these puzzle pieces of the real life accident scenarios that are happening and how our technology can help with that. How do they link to brain injury? What do we know now, and how can we help with that? And also compare ourselves to products that people might know — different commercial brands in those different fields.

And in this whole process, we also have to keep questioning ourselves: how do we best test something? Because as I told you earlier, cycling was a very good first step, but now to prove ourselves in all these other different fields, we also have to understand what is the right method. So, yeah, for me, it’s answering those gaps of knowledge and communicating with that wide range of stakeholders.

Jamie: Building on that, there’s a lot of things I’m excited about. If I had to choose two, firstly launching in different verticals, particularly, in new professional sports. It’s going to be huge for us, the visibility and the technology just looks great, the partnerships are very strong.

Then the other one is defining the gold standard for helmet safety. From publicly available data, the average risk of concussion of a certified bicycle helmet without anti-rotational technology is about 60%. So if you fall of your bicycle and hit the ground headfirst wearing a helmet without any advanced technology, you’re more than likely to have a concussion, and that’s at about 22-ish kilometers per hour now?

Maria: 23, yes.

Jamie: So not even that fast… For decades the industry has tried to persuade consumers to wear helmets and inadvertently communicating that all helmets which pass standard are safe. I assume for most people, if you ask, “are you safe wearing helmets?” They’ll say “oh, yeah, of course I’m safe because it passes a standard.” The reality is that current standards cannot guarantee safety and no helmet has equal safety.

What we’re doing is reducing that risk of concussion down to 15%. It is a major, major improvement, and I’m really looking forward to making that as clear as possible in the market to hopefully redefine what standards look like and what safety looks like. And I think if we can do that really well and effectively, hopefully we’ll make a big difference to the outcome of a lot of these injuries that are still happening.

Cam: Well, apparently I lied because I actually also have a follow-up question for Maria, well for both of you, in terms of expanding into different verticals here, while kind of also linking back to testing your product inside of a walk-in freezer. It seems like a lot of other verticals are going to have different environmental considerations: obviously different operating speeds and impact forces, and different operating environments that could be dustier, colder, hotter, etc. How do you think about upgrading your overall testing both in the lab and in the field to adapt your technology into these different environments?

Maria: Yeah, very interesting question, that’s definitely something we’re looking to do! Well, if you’ve seen the helmets being tested at the moment, you know that they include just a head form, which is a molded head, added into a helmet and dropped from height. In the past, it used to be on a flat surface, which is what the linear testing that we’re talking about is, and now testing is also done on an angled surface, to test the linear and rotational response of the system upon impact.. And that’s all well and good, and that allows us to look at rotation and concussion risk, but obviously, a real person is not just a head.

So, as an example, we are making steps towards looking into the effect of the neck in the whole process. That’s where engaging with the academic community that discusses spine injury biomechanics — and how the head and the and the neck come together for that testing — is very useful, both from a modeling perspective and an experimental perspective to understand the limits of each of these approaches. Then further on, even if you were to test with the neck, the current setups require the lower part of the neck to be stable, but again, that’s not how a person falls — so how do you add a body into such a setup? How would you test with that?

So we’re constantly thinking about upgrading our testing setup so that we can test in more representative conditions. We have active partnerships with academics in each one of those fields, but also on the modeling side, so that we can validate the results that we’re getting and assess what they mean.

And depending on the vertical, that testing could change. So, as one example, for a construction helmet, one important thing is that the majority of non-fatal accidents happen from objects that just fall on people’s heads instead of the head hitting a surface. So that is a completely different testing setup that we had to come up with. Or when considering snow helmets, you can imagine that the surface that people hit their heads on is completely different to cycling. So maybe developing an in situ drop tower that is on a slope would be the challenge. I think a very important part of developing those new verticals is questioning the test setups themselves and having that expertise in-house.

Jamie: So what Maria is really saying is she needs to go to a ski slope somewhere.

Maria: I would love to, if we need that person.

Cam: Chamonix might be the perfect place for some testing! Well, this has been extremely interesting. Again, thank you guys both very much for taking the time here!

Jamie: Awesome. Yeah you know, obviously we’re super excited, but we think there’s a big story to tell still, not only in how RLS is performing, but also just the current standard of helmet safety — it’s dangerously low and there are big strides to make!

The Winter Olympics are finally upon us once again!

Today marks the opening ceremony (and third day of competition) for the 2026 Winter Games held in Milan and Cortina in Italy. Although I may be in the minority with this opinion, I generally think that the events in the Winter Games are far more entertaining individually than those in the Summer Olympics. Sure, watching people throw a small sphere as far as they can, or running quickly for ten seconds or 2 hours in the summer is cool, but watching a crazed maniac go face-first down an ice track at 90 MPH, seeing a woman do a backflip with blades attached to her feet, or witnessing a Scandinavian dude tear down a ski hill only to whip out a rifle and shoot down range? That is an unbeatable and unique experience. (Yes, I know I am cherry-picking, I never claimed to be an unbiased observer of the world).

I can’t wait to quad- or octo-box the Peacock app consistently for the next few weeks (again). In particular, I’m excited to check out the newest sport being introduced to the Winter Olympics and some of the new events making their debut this year. So much so that I wanted to draft a quick overview of the new sport and events, and share at least one interesting technical or performance detail about each. There’s a total of seven items to cover, so I’ll keep the overview and facts for each individual event brief in order to save as much time as possible for actual event viewing.

Hopefully even this brief overview of the new sports on offer gets you excited for the games, or at least gives you some snippets for the start of Zoom call chit-chat. If you need a refresher on the overall schedule, the Olympics website is a great place to start! In the US, NBC has a local TV/streaming guide as well.

Let the games begin!

New Sport: Ski Mountaineering (“SkiMo”)

SkiMo is the only completely new sport to be introduced at the 2026 Winter Olympics, with the most recent prior being Skeleton at the 2002 Salt Lake City games. At the highest level, SkiMo involves traversing up and down a slope (or full mountain) on skis. There are many distances the sport encompasses, and the Winter Olympics will host two of them: the Sprint Race and the Mixed Relay Race (one man and one woman per team). In both events, athletes will “skin” up a slope segment on skis and “bootpack” up another staircase segment with their skis on their back before throwing the pair back on and skiing down.

Since it’s a new sport, it’s worth watching this video summary of the sport and race format!

For both events, the total vert gained is ~230-300 feet, and the crazy thing is they complete all of this in just about 3 minutes total up and down. If this sounds impossible in traditional ski gear, that’s because it is! High-quality race SkiMo gear is incredibly lightweight, with a set of boots and skis often weighing just under 5 pounds total. Every time I skin up a resort or backcountry, I regret not having this light of a setup…

This localized ABC coverage actually has perhaps the best written summary of the sport I have seen so far if you want some more info on the event itself!

New Events: Many!

In addition to SkiMo joining the Winter Games as a brand new entry, numerous existing sports added events, many of which added additional team or sought to level gender-parity!

Skeleton Mixed Team Relay

The Skeleton Mixed Team Relay pairs one man and woman from the same country together for a “relay-style” skeleton race where each team’s time is the sum of their individual runs. Unfortunately, I do wish that there was a live relay for this type of event, but the two times are taken in distinct, separate runs down the mountain.

Something that surprised me but might be obvious for a gravity-conveyed racing sport is that skeleton sleds are very heavy. Men’s sleds can weigh up to 43 kilograms (~95 pounds), and women’s sleds can weigh up to 35kg (77 pounds). The sleds can also reach up to nearly 4’ in length! This year, the only Winter Olympian from Puerto Rico is competing in the Skeleton this cycle!

An additional fun fact: this is the sport I’d be least likely to do out of any Olympic Sport, either winter or summer!

Team Combined Alpine Skiing

This is the first true “team” event in Olympic alpine skiing history, which is quite exciting. In this event, a team consists of two skiers (separated by gender): one completes the “Downhill” course, and a second athlete completing the “Slalom” course. The total team time is the sum of these two individual events, and the overall goal is to show off the skiing breadth and depth of a team. Each nation can field up to four teams total, and it will be interesting to see how teams are constructed for the best shot at winning. Even though it’s a combination of prior existing events, I think it’s a fun way to drive more team spirit, and lets teams with more depth instead of one star vie for medals!

Although it’s not featured in this event, my favorite ski event is still the “Super G” which is a large-scale slalom race that forces racers to make steep turns while still traversing up to 2,000 feet of elevation drop in the span of 90-150 seconds, which is absolutely insane. I really hope that Lindsey Vonn can be healed up enough to compete after her recent crash (and ACL rupture).

Freestyle Skiing Dual Moguls

Freestyle Skiing Dual Moguls is maybe my favorite new addition to the event roster this year because of how funny the premise is when compared to other sports. In traditional Freestyle Skiing, one athlete traverses the mogul sections smoothly, does tricks off jumps, and tries to reach the bottom as fast as possible (they are scored as a sum across these three disciplines). The athletes go one at a time and are scored individually in rank order.

In Dual Moguls, which sounds incredibly dramatic, two skiiers actually do that same task side-by-side and compete directly with one another in a bracket-style tournament. It kind of needs to be seen to be believed:

My favorite part of this event is thinking about translating it to other performance-scoring-based sports like gymnastics or figure skating or high dive. Imagine two people doing bars, floor, or short program routines right next to each other instead of by themselves, and how fast you finish also matters in addition to how well you perform. It would be insanely fun to watch, but ridiculously chaotic at the same time, and it gets even funnier to think about for other types of individual sports!

I’ve got no fun tech fact for this sport because I got sidetracked thinking about funny comparisons, but we’ll recover.

Women’s Large Hill Ski Jump

There are two classes of ski jumping, for which the goal is to fly as far as possible while also maintaining good style (who knew): “Normal Hill” and “Large Hill.” Prior to this cycle, women’s events were restricted to the Normal Hill at the Olympics, and in Milan-Cortina that changes, with women’s individual events also taking place on the Large Hill!

In Milan-Cortina, the Large Hill has a height of 141 meters (~420 feet) and a K-point of 128 meters, when the landing zone begins to flatten out. I for one cannot imagine blasting down a ski slope at 60 miles an hour and covering 400 feet of horizontal distance in the air. I feel like my spirit would simply leave my body at some point near the apex of my arc through the air.

In 2024, the longest ski jump of all time was set, although not on a regulation ski jump hill. Regardless, it’s still an unbelievable achievement to watch, and highlights how finicky the actual flight can be!

Men’s Super Team Ski Jump

Despite the name, this is “Super” Team event is actually a slightly downsized version of the previous team event. This is a team-based ski jumping event on the Large Hill, but the number of participants per team has been reduced from 4 to 2. Unfortunately, right at the start of the Winter Games, men’s ski jumping has been hit with a rather hilarious supposed cheating scandal, so we’ll see if the event itself is affected at all during its inaugural run!

Despite the somewhat funny accusations, it is pretty unbelievable how tightly-controlled the suits are for ski jumping. The International Ski Federation (FIS) has extremely tight and well-regulated requirements for how thick and large a jumping suit can be because lift and drag are such major components of the sport’s performance and outcomes. Pretty much all dimensions of a jump suit are monitored to within centimeter precision, and tightly measured during or after events.

Women’s Double Luge

Technically, Double Luge has been a mixed event since its inception , but has de facto been a men’s-only event for its entire duration. The sport is just like luge (laying on your back and sliding down the ice slide), but in Double Luge, you have two people just laying on top of each other, for some reason. This year, the women “lugers”(?) will have their own event, and I’m super excited it’s driving towards gender parity at the games. However, I firmly relate to the author of the following Youtube video about Double Luge when I question why this event even exists in the first place…

The fun fact that surprised me is that the luge is actually the fastest of the sledding disciplines on average, and Germany absolutely dominates the sport, with 11 of the 16 gold medals in Double Luge at the Olympics to-date.

There’s the bell!

At the highest level, I see many of these same trends persisting through 2026, especially as the actual business of sports from the pro level down continues to boom. However, I do want to highlight a variety of trends that I foresee emerging or retracting this year, some of which are very specific to a certain technology or business vertical. If you’ve followed the Sports Innovation Roundup through the first few weeks of the year, you know there’s already been a lot of movement in the sports tech space specifically, so hopefully I can now give some advance notice about what’s to come beyond February!

I’ve used the same set of innovation application categories as the 2025 review, but some of the trends below (particularly the tech developments) relate to many different applications, so I’ve done my best to call out potential cross-industry application where possible. With so many thoughts swirling around my head, I’ve tried to keep each prediction relatively short without losing any important details. However, if there are any major trends or developments that you think I’ve missed, please let me know— I’m always happy to chat in the comments or over email!

Let’s dive in!

Fan Engagement

Technologies primarily designed to enhance fan experience, attention, or emotional engagement with sport.

• Live AI-based commentary translation enables rapid internationalization of broadcast organizations. Live, multilingual captioning and translation has the chance to move from an experimental add-on to a real growth lever for media rights-holders. Specifically, I think events like the Winter Olympics in Milan and the Men’s World Cup in the summer could serve as the perfect inflection point where domestic broadcasters around the world realize the power of these new tools and aim to deploy them rapidly. Of course, the tech companies creating these capabilities like CAMB.AI will benefit, but rapidly-internationalizing broadcasters like DAZN may see the biggest boost.

• “Co-streaming” goes mainstream and is codified with the first real media deals through leagues themselves. Co-streaming (a creator adding their own real-time commentary and video to a live event) has become increasingly popular, with many YouTube and Twitch channels garnering followings in the millions. Amazon Prime’s Thursday Night Football has attempted “alt-casts,” and I think 2026 is the year that leagues and broadcasters begin to let online groups run their own broadcast in a more official capacity. This trend runs counter to the proliferation of AI-based translation if leagues allow co-streaming in different languages, and the ramifications could be crazy if this prediction comes true!

• Personalized dynamic pricing reaches the live event ticket market (sadly). Dynamic/tiered pricing keeps creeping further into all walks of life from airline seat pricing to restaurant menu costs, and the most recent iteration that targets pricing for specific individuals based on algorithmic predictions has slowly started to spread. In the 2nd half of 2026 as major pro leagues start up again, I think online sports ticket sales become the next victim of this trend, and whatever business jargon language that’s used to justify it falls on deaf (and angry) ears.

• Sports gambling and prediction markets (also sports gambling) regress in total engagement. I think there’s many reasons to believe a sports gambling “regression” is on it’s way, so this might not be a bold prediction, but it could be a year or two early. It’s possible the overall sector gets squeezed by unit economics from customer acquisition costs in a more competitive landscape; the increased scrutiny of prediction markets leads to dramatic regulation that also affects sportsbooks; or the consolidation ongoing in the sports betting world leads to increased consumer costs and drives down engagement. A major challenge may actually come from pro leagues that have a lot to lose with these users.

• The surprise success of Heated Rivalry sparks a series of intentional moves by pro leagues to launch shows. This prediction is a bit more tongue-in-cheek than the others, but the surprise success (and for the NHL the free marketing) of shows like Heated Rivalry or movies like Moneyball can help boost actual sports viewership. I could easily see leagues lean further into working explicitly towards creating viral fictional shows and movies about their own league— they may be more Rom-coms or maybe more direct marketing ploys like the F1 Movie!

• Online sports streaming begins to become more popular than linear TV for specific segments. In 2025, Amazon posted league-record viewership for the Thursday Night football game, and in 2026 I think they finally have viewership reach the average for an NFL game at 18.7M people (which is crazy). So many of the arguments in favor of linear TV being more popular are continuing to erode, and in 2026 I think we finally start to see the dam break, although it will still take years for streaming to dominate.

Sports Data & Operational Infrastructure

Products and services designed to enhance the actual execution of a sports organization (e.g. teams, leagues).

• Maturation of use-case-specific “Agentic AI” already starts to impact process-, analytic-, and engineering-heavy sports in 2026. At the simplest level, I view most “agentic AI” offerings as highly-effective and adaptive automation tools that can integrate with both software and hardware process flows. Across the world of sports, these types of opportunities pop up everywhere: from post-game analysis and data cleaning in the NBA to highlight creation and management in the MLB to car design and strategy adjustment in racing sports like F1. Agentic AI tools have the opportunity to dramatically reduce the manual burden for everyday operations and engineering activities, which could provide meaningful reductions in time-to-decision or time-to-implementation.

• AI-based in-game team management enters collegiate and low-level pro sports with a clear path to major leagues. Despite the fact that I propped up the abilities of pro coaches (at least in the NFL) in an article late last year, I also maintain that I think a well-crafted algorithm can call plays just as well as most coaches, and probably better than everyone except the high-tier professionals. Whether or not this is realized through the utilization of neural-network-based decision making or not, I think 2026 might be the year when coaches start to receive and often follow suggestions from a computer. I don’t expect many full staff replacements in 2026, but I don’t think that prospect is too outlandish in 2027+.

• Leagues begin to toy with the concept of dynamic scheduling. Quick question: why do pro leagues establish a matchup schedule at the start of the year and leave it the same no matter what happens during the season? Certainly there are logistics and fairness arguments to be made, but I think modern travel and analytics tools have reached a point that these can be mitigated. I also think emerging matchup formats like the Swiss-system and a desire to broadcast more compelling matchups regularly may push some pro leagues to actually try dynamic scheduling based on team strength or popularity mid-season. Is it an out-there prediction? Definitely— but I think it’s one that bears consideration!

• Investment management platforms become a core component of athletic departments as NIL matures. I’ll be honest: I have no idea what the college sports landscape will look like in ten years. For all I know the major sports like Football and Basketball could be completely detached from college affiliation and have formed their own leagues. In the short term though, colleges need to tackle the world of NIL deals and (now) direct payments to athletes, for which they are wholly unprepared. The lack of infrastructure for the emerging complex financial and compliance ecosystem should naturally breed a whole new tranche of companies and products providing marketing, deal tracking, reporting, auditing, budgeting services, and much more!

Player/Match Tracking

Technologies that measure or capture player, object, or match-level data as primary outputs.

• Despite continued proliferation, there is a mini reckoning about the accuracy of computer vision systems in pro sport officiating. I wrote a whole article about the topic of computer vision system accuracy last year, and despite rapid advancement in the space, most systems still can’t reliably achieve sub-1/4” accuracy for key applications like calling balls/strikes, tennis shots in/out and offsides in soccer. I see no reason to believe we can’t eventually achieve ~1mm accuracy for most systems, but in the interim, I think there’s some pushback on automated officiating systems in 2026, especially due to perceived accuracy issues.

• Both pose modeling- and convolutional neural network (CNN)-based computer vision systems grow in popularity, though for different applications. These two analysis pipelines demonstrate very different opportunities and weaknesses that actually complement each other well and I expect products using both to flourish this year. Pose modeling can provide real contextual movement data and can provide high accuracy, but often struggles with extremely rapid motions, higher latency, robustness under occlusion, and hardware implementation cost. Conversely, well-optimized CNN pipelines can provide live data from complex and rapid movements extremely quickly, even when partial occlusion of the camera occurs, but provide little-to-no context or reasoning. Perhaps the combo of these two technologies will eventually reign supreme!

• Detailed, automated match tracking for youth sports lands in 2026. Youth sports (including high school) presents the perfect product wedge: there are tons of games, scarce staff time, a growing online viewing ecosystem, and huge appetite for shareable highlights and “good enough” analytics. A single-camera computer vision workflow that produces live officiating tools, box scores, clips, and basic match tracking is now technically feasible (and many products are moving towards full implementation), and highly monetizable. Soon, a set of four camera systems may be able to run the umpiring for a baseball field tournament all day without any manual intervention!

• GPS-based athlete and ball tracking for team sports recedes into the background, and local positioning system (LPS) products see a short-term boost before receding as well. Despite a struggle to provide millimeter-level accuracy, even rudimentary computer vision systems may soon provide much more accurate player activity data than GPS/Accelerometer-based products, and even LPS products that use UWB radio frequencies to triangulate player locations. This prediction may not be aimed at the general audience so much as any investors looking at these available technology stacks!

Human Performance Enhancement

Developments supporting improvement of human performance, both physical, and mental.

• Major breakthroughs in thermal management occur on the equipment level and get used in endurance events. It depends on the sport and conditions, but up to 75% of an athlete’s total energy output can come from thermal energy as opposed to mechanical energy actually propelling themselves. The Nike All Conditions Group (ACG) started to play with very lightweight-but-shaded fabrics in 2025 and cycling teams have tried to thermally-manage athletes with advanced tech (e.g. socks full of ice) for years. In 2026, I think we finally start to see athletes take heat training more seriously, and more importantly, thermal vests and other new products on the market are used by athletes during actual endurance races, especially those where added weight matters less.

• Running brands continue focus on aerodynamics. Aerodynamics remains the highest leverage “free speed” domain in many racing formats, including well-known sports like cycling and F1, but even in running across most pro distances. The biggest proof of this might lie in the fact that brands started to look at making their marathon shoes more aerodynamic in 2025, and in 2026, I think we start to see running go the way of cycling and triathlon: athletes running all distances are in aero-designed gear from head to toe with style choices to reduce drag.

• 10 major endurance world records are broken in 2026. Given my prior predictions about performance improvement and continued learnings about sports nutrition, I think many world records on the level of an olympic running/swimming event or major race like UTMB or the hour record in cycling will be broken this year. I think this trend is likely to continue for many years, especially in women’s athletic events, where we’re finally moving past utilizing men’s training programs to programs that are more tailored to physiological optimization!

• Artificial performance enhancement doesn’t get off the ground as a standalone competition category (yet). Despite the continued hullabaloo about the Enhanced Games launching in 2026 and continued doping scandals across the world of sports, I don’t see the world yet being ready for no-holds-barred performance-enhanced sport. Maybe I’ll be proven wrong and 2027 will be a year of exponential growth for the field, but I’m a bit skeptical…

Consumer Products

Sports-related products and services sold directly to individual consumers rather than teams, leagues, or media companies (i.e. everyday gear/equipment).

• The first name-brand sub-$150 carbon-plated shoe hits the market. If my research serves me well, the Nike Zoom Fly 6 is the cheapest carbon-plated trainer on the market at $180 MSRP. We’re reaching the point now that economies of manufacturing scale and upgraded assembly techniques should lead to even cheaper carbon-plated shoes as long as demand continues to increase in parallel. It might be a minor win for the overall running community, but I think it could be a great example of technology democratization and I’m excited to finally try a pair soon!

• Shoelaces get replaced with another fastening mechanism for high-end running shoes. As companies look to minimize product weight and maximize aerodynamic properties and comfort, I think it’s easy to predict that the era of the shoe lace for elite running shoes is coming to a close. High-end shoe companies like Mount to Coast and Altra have already begun to experiment with quicklaces, BOAs, and other technology to replace shoelaces. By the end of 2026, these shoelace alternatives become mainstream in the highest-end shoes for most brands.

• Screen-less health trackers experience a dramatic decrease in size leading to a spike in demand. WHOOP has really pioneered the screen-less health tracker, and the market is not absolutely flooded with competition. Development on these devices is so common that it’s only a matter of time until there’s a step-function change in size, even if the capabilities decrease slightly. In 2026, I think we see the first few products to market that make good on this promise and the size drops by 50% in all dimensions, which leads to much higher demand from non-committed users. Hey, it we can fit everything into a ring, why not a Livestrong-size band?

• A L.A.B.-putter equivalent emerges for at least three other sports in 2026 (and multiple are acquired within three years). L.A.B. Golf succeeded because it leveraged modern manufacturing availability, picked a very niche sports product, came up with a unique physical innovation, and marketed their product online perfectly. The same process took place with TRUE Hockey, and I see no reason that this pattern can’t repeat itself across other equipment-heavy sports including hockey, baseball, lacrosse, or even golf itself (again)! In 2026, I think at least three companies will quickly emerge without any venture backing and become ubiquitous in their respective sport. Looking forward, I think multiple (or all) of the companies will eventually be acquired by existing market players.

Bonus: Company & Technology Development

A cross-cutting category for how innovations in sport are developed, marketed, and monetized.

• League-operated incubators and investment funds continue to proliferate. Leagues have learned that structured pilots are a cheap way to (1) de-risk vendor selection and (2) shape product roadmaps of startups toward their own needs (while potentially taking a stake in their company itself). Incubators like NBA Launchpad and startup challenges will expand more rapidly in 2026, which additional broadcast and media companies really taking a dramatic step up in early technology engagement (perhaps even through some acquisitions?)

• More Big-4 US pro sports teams are sold in 2026 than the past 3 years combined. From 2023-2025, a total of twelve pro sports teams across the MLB, NBA, NFL, and NHL saw their majority owner change (~8% of teams total). With increasing demand for team ownership and a rapidly-changing financial landscape around pro sports, I think we see another twelve team sales in 2026, or at least the first-ever double-digit sale year. If this does happen, I do think it could have a dramatically negative effect on smaller upstart leagues, but that is a bit more to-be-determed!

• There’s another increase in funding for sports-related companies as live events become a further lynchpin of the media landscape. The argument to investors is getting simpler: live sports and expereiences remains one of the few reliably appointment media categories while the rise of (and pushback against) AI-generated content continues to devalue non-live events. Stack on top of this the ubiquity of social media and the increasing to desire to attend exclusive events like games and concerts, and it’s easy to see how the next decade+ could demonstrate non-linear growth in the sports industry more broadly.

Twelve months from now we can reflect on how well I actually predicted the future! I’d honestly be happy with a 50-70% hit rate on predictions…

In general, I would say that sports business dominated the news in 2025, rather than technology and product innovation, in part because the headlines in that space were so dramatic. By my own count preparing the Bi-weekly Roundup throughout 2025, investors raised over $11 Billion* to funnel specifically into sports-related businesses, with the vast majority of this being aimed at specific team investments and acquisitions— and this figure doesn’t count direct acquisitions of sports teams by private (or quiet) groups. This number represented a notable uptick from 2024, despite the fact that 2024 had two separate $4B+ fund creations.

*Note: I feel my research is generally comprehensive when it comes to sports tech startup news, but I am sure there are more definitive sources when it comes to the fund creation and business aspects of the industry (especially with so much indirect funding through the Saudi PIF), so take this number with a grain of salt as a floor value for 2025!

In tandem, PE funds and other companies made major strides in consolidating the market for many market segments, with at least 30 separate sports-technology adjacent company acquisitions occurring in 2025 (more on these later). Some of these numerous competitor acquisitions were the result of over $3.1 Billion being distributed to sports-related companies in 2025 (although again, with a major focus on teams and leagues). Compared to 2024, fundraising and investment seemed to increase across the board, and some great business-oriented summaries like SportsPro’s 2025 in Review that provide additional color on the highlights of sports business.

This list of 2025 trends, however, will focus (mostly) on sports innovation: the combination of technology invention and commercialization/productization. The trends are grouped by their application in the broader sports industry, with use-cases ranging from fan engagement to consumer product design, and everything in between. With so many different development threads to summarize, my description of the trends stay relatively high-level, but rest assured that a lot of unique and meaningful technology and commercial developments have occurred beneath the surface to support these high-level trends. If any particularly topics interest you, I’m more than happy to subject them to Geek Locker article-ization (similar to my computer vision overview)!

This recap should set us up well to look at what’s to come in 2026 and beyond, for which we’ll be making predictions in the next article. There’s a lot to cover here, though, so let’s jump in!

Fan Engagement

Technologies primarily designed to enhance fan experience, attention, or emotional engagement with sport.

• The technology and infrastructure for alternative, animated broadcasts reached the point of long-term viability. Sony and ESPN renewed their animated kid-focused simulcast endeavors through the Beyond Sports initiative, and companies like Genius Sports launched an animated replay platform that can serve as the basis for similar products in the future. Whether or not these become ubiquitous across broadcast platforms is to be determined by actual viewership feedback, but the tech stack has reached a point of maturity that should enable large-scale testing.

• The tip of the generative AI iceberg revealed an underwater base with questionable depth. On the shallow end, Fanduel launched a sportsbetting advisor chatbot early in the year (still not sure how this is legal). However, startups like Lingopal.ai raised significant $14M through a Series A fundraise. Dozens of new products and companies launched on the platform of generative AI with actual utility somewhere between a chatbot managed by a sportsbook to a live translator (which is awesome).(End convoluted iceberg analogy)

• New sports gambling mechanisms hit the market, while existing guardrails began to disappear. The talk of 2025 was certainly the rapid growth of “Prediction Markets” such as Kalshi and Polymarket, which represent a repackaged version of gambling that somehow skirts existing anti-gambling regulations. In addition, existing sportsbooks expanded their product offerings, with a major focus on micro-betting with companies like Kero receiving investment and gaining traction in the market. Who knows what 2026 has in store, but if you were hoping that sports gambling proliferation would decelerate in 2025, you were probably sorely disappointed.

• Sports cards and memorabilia continued to rise in popularity (and value). Trading card platform CollX started the year by raising a $10M Series A, and industry staples like Topps released new high-value card types across a variety of sports, including soccer! Crazy individual news stories about individual cards being sold for thousands or millions of dollars have driven a renewed interest in little pieces of cardboard, although the complexity of making the cards themselves continues to grow!

• In the background, crypto-centric fan engagement products and companies had a quiet year (even including the sports-card platforms). Perhaps the most note-worthy piece of news was crypto platform Tether acquiring a stake in Juventes Football Club. Despite the relative lull, chance definitely remains that crypto-related technologies become a core component of fan engagement in the future.

Sports Data & Operational Infrastructure

Products and services designed to enhance the actual execution of a sports organization (e.g. teams, leagues).

• PE funds and startups alike began to enact plans to take over the massive swath of youth sports market opportunities. There’s almost too many news stories to cover here, but for a taste: Dick’s Sporting Goods invested $120M in Unrivaled, a youth sports league platform; startup Ankored raised $4M in seed funding for their youth sports compliance tools; and Rocket Youth raised $100M for their youth sports platforms, among many other deals and events throughout 2025. While PE funding is here to stay in youth sports infrastructure, CBS did sell their high school broadcast products to PlayOn, so maybe the youth sports media landscape is yet to take off too…

• The advent of wild west name-image-likeness (“NIL”) and direct investment rules in college athletics generated a ton of new product and service opportunities. While some groups like Chiron Sports raised $150M just to invest in business opportunities in the burgeoning space, companies like MOGL launched new products to help athletes navigate their new reality, including an AI-enhanced software package that links athletes with potential sponsoring brands. There is no putting proverbial toothpaste back in the tube for college athletes being paid.

• Companies raced to become the gold standard for team and league logistics management, resulting in now clear winner and numerous viable options. Leagues like the NHL and NBA got in on the $40M funding round for Fastbreak.AI, a company that deploys intelligent scheduling and logistics tools. Similarly, La Liga teamed up with Globant to deploy advanced AI tools for their league infrastructure management. Many of these providers have moved into the pro and youth sports world simultaneously in the past year, so many platforms are available for those looking to establish or upgrade their league and team management tooling.

Player/Match Tracking

Technologies that measure or capture player, object, or match-level data as primary outputs.

• Computer vision became the gold standard for in-game athlete and event tracking. Products like ultra-wide band (“UWB”) positioning systems, and others with inertial measurement unit (“IMU”) sensors, still make sense in certain applications for tracking balls and athletes in 3D Space. However, the adoption of computer vision systems (like Sony’s dominant Hawk-eye system) are now used by the NFL to call first downs, by the MLB to verify balls and strikes, and by FIFA to make offsides calls. Even the X Games spun out the company Owl.ai to use computer vision systems and pose modeling to analyze half pipe runs. For the time being, the future of ball and athlete tracking lies with computer vision engineers— they’re extremely flexible and can be used for everything from officiating to animated replays for fans.

• In parallel, computer vision products began the road to financial democratization. On the specific sport end of the spectrum, Norway-based SportAI raised a pre-seed, and a $3M seed round in 2025 to bring computer vision and automated scoring and analysis tools to raquet sport consumers. Jabbr also raised a $5M seed round to automate boxing match scoring and broadcasting. Other more general sports-tracking computer vision companies such as Pixellot and ReSpo.Vision both raised capital to make their tools accessible to teams and players not operating on budgets with tens of millions of dollars. We’ll definitely talk more about this category in the 2026 predictions…

• Giants in the player/match tracking market made moves to consolidate new competitors and features. If there’s reason to be optimistic about a lot of computer vision and match analysis providers competing against one another to drive innovation and lower costs, there’s also reason to be concerned about the industry staples removing those forces. In 2025, Genius Sports made multiple acquisitions, including the Sports Innovation Lab for fan engagement. Youth sports staple HUDL also acquired multiple competing and supplemental companies, and so did Catapult Sports, who acquired Perch and IMPECT in 2025 alone. As they say, it’s a dog-eat-dog world!

Human Performance Enhancement

Developments supporting improvement of human performance, both physical, and mental.

• Carbon plates found their way into every shoe, but haven’t started getting cheaper (yet). Hoka wasn’t the first, but followed in a long line of companies adding carbon plates to trail running shoes, while nearly every established shoe brand now has multiple different carbon-plated shoe options, including some “daily” trainers. Reebok even put one in an indoor training shoe! Eventually, actual manufacturing and assembly tech will become ubiquitous enough that companies may start lowering the prices of these premium shoes, though it may take a new upstart to really set that process in motion!

• Companies began to get serious, and more creative, about capturing marginal gains in running. More specifically, companies began to start leaning into gear design decisions that actually deferred aesthetics in favor of performance. Nike ACG’s Radical Airflow shirt actually maximizes breathability by using a weave that makes it look like a doily, while Salomon recently debuted the S/LAB Phantasm 3 super shoes that have an upper cover designed for aerodynamics! Both of these products are joined by many more, and while they look funny, they likely actually assist the brands’ athletes in competition (and they also create a marketing buzz which is a plus too).

• Companies are betting big on fans getting interested in the limits of human performance again. Nike revived its high performance aspirations with Faith Kipyegon’s 4-minute mile attempt, and Kilian Jornet completed a ridiculous assault on the highest peaks in the contiguous United States in the early fall. The most extreme version of this trend comes from a company centered exclusively on pushing limits: The Enhanced Games, who not only raised capital in 2025 ahead of its inaugural event this year, but has aims to go public through a SPAC as well. It’s exciting to see companies pushing human performance further with technical and performance innovations (outside the drugs)!

• Longevity became the new catch-phrase for health-related products and defined a significant portion of feature development. Multiple investment funds like Next Ventures and CoPeace raised $60M each to funnel into health, wellness, and sports startups, while dozens of companies like Somnee (sleep improvement) and Hexis (personalized nutrition) received funding from these and similar entities in 2025 too. In tandem major companies like WHOOP launched their new hardware 4.0 alongside a new blood-testing laboratory offering, and Garmin launched the Index sleep monitor.

Consumer Products

Sports-related products and services sold directly to individual consumers rather than teams, leagues, or media companies (i.e. everyday gear/equipment).

• Popular activity-tracking service providers made moves to flesh out their features and raise walls around their user base. Strava was undoubtedly the poster child for this effort, by acquiring Runna and then The Breakaway for running and cycling training respectively. Garmin even followed suit by acquiring MYAPS for event timing, before Strava sued Garmin for completely unrelated and erroneous reasons. Without realizing it, our current golden age of cross-compatibility of workout tracking hardware and apps may soon come to and end.

• The success of L.A.B. Putters paved the way for new niche brands to takeover specific equipment verticals. LAB Putters simply makes putters that work well, and were acquired for a reported $200M after just 7 years on the market. I think the move set in motion a relatively hidden innovation trend that we may see actually become a consumption trend in the years to come: niche hardware products that dominate a specific slice of a category. Selfishly, I hope this comes true because I’d love to see more hardware innovation on the market by new companies!

• Sports gear companies continued to proliferate their offerings to cover every product under one brand. This trend of companies launching a version of every product within their market is not new, and I’ve mentioned it multiple times on this newsletter. Unsurprisingly, it continued this year, with companies like Vuori launching new winter sport outerwear, and even companies like Rivian deciding to break into the e-bike market. I think this trend runs in conflict with the previous one, and if popular brands dilute their resources across too many products at once, they may lose share to new brands focused on just one or two— it’s a classic conundrum!

• In-home smart equipment experienced a plateau in adoption and new product launches. To my knowledge, the major in-home gym product release of the year was the GROWL in-home boxing system. Peloton iterated on their bike and treadmill design, but also launched their own buy-back used equipment marketplace which might not be the biggest vote of confidence for the overall segment. Brands have struggled to find a good product-market fit for at-home smart gym equipment in the wake of the COVID-19 pandemic, and it showed this year… but I still think there’s a ton of opportunity moving forward in this market.

• It might be anecdotal, but it felt like brands collaborated on special edition products more in 2025 than previously. This trend may be the result of the limited-edition marketing run that Stanley undertook a couple years ago, and it can lead to some very unique and interesting products. Nike and SKIMS teaming up was a major move, but I preferred weirder collaborations like the Columbia-Kith Nippon ski gear, the Redbull-TaylorMade pitching wedges, or the Hoka-MAAP trail shoes, among many others. If it means more creative partnerships on products moving forward, I’m all about it!

Bonus: Company & Technology Development

A cross-cutting category for how innovations in sport are developed, marketed, and monetized.

• Individual teams and leagues slowed down in their launch of in-house incubation and investment entities. In 2024, it seemed like every individual team and league wanted to launch their own startup incubator and/or innovation competition. This trend seemed to slow dramatically in 2025, although I think it’s partly the result of most leagues and interested pro teams already having deployed some sort of program…

• Unspecified and bridge funding rounds continued to play a major role in the startup space. In 2024, about 40% of all funding rounds for sports tech startups strayed from the traditional Seed—Series A—Series B—Etc. pattern, and that ratio stayed nearly the same for 2025. It’s a bit difficult to pinpoint the exact cause, and it’s likely that a combination of special-use funding rounds (e.g. for acquisitions) and perhaps some bridge funding or down-rounds caused companies and investors to eschew traditional naming convention.

• Private equity firms played hot potato with major consumer brands across different pieces of the sports product landscape. Large companies like outdoor gear company Helly Hansen and the helmet brand UVEX were acquired by new PE ownership, while two of the biggest hockey gear brands in CCM and TRUE Hockey were both acquired by private groups. Heck, even Footlocker got purchased by Dick’s Sporting Goods! It was a year in which brands all across the design and supply chain got shuffled to new ownership groups, which might yield some interesting changes next year…

See you in a couple weeks for our 2026 predictions!

Although I mainly pose this question when one of my fantasy football players is being ignored during a game, it turns about to be quite an intriguing question. I also think that we can construct a mental framework to roughly estimate how much better a professional play-caller is than someone choosing the order of plays at random without diving into an in-depth data analysis project. The efficacy of “bogosorting” (my favorite inefficient joke sorting algorithm) the plays to find an optimal order may be dubious to begin, and at the end of this article I’ll also propose a few pathways for quantitatively estimating the efficacy of random play-calling, and leave the actual work up to the research community (or me sometime in the future).

The framework for coarsely estimating the presumed positive impact of an actual NFL offensive coordinator calling plays comes in three steps. Note that I’m assuming that the actual play-caller already has access to a well-coordinated team and a pre-set list of legal offensive plays at their disposal that include a normal set of runs, passes, punts, kneels, trick plays, and so on. The only thing that the play-caller is doing is picking which play to run.

1. Determine a theoretical basis for the existence of an actual spectrum of skill selecting offensive plays

2. Estimate a skill—impact curve for offensive play-callers in terms of shape and magnitude

3. Place random play-calling and offensive coordinators on this curve!

These three relatively simple steps cut out perhaps months of research, while providing an answer that might be roughly 70-80%+ accurate. Instead of the 20-80 rule where you do 20% of the work to get 80% of the answer, this process feels like it embodies a 2-70 rule that’s yet to be popularized. These steps also harken back to one of my favorite mental models for determining the importance of different decisions by thinking about extreme outcomes (shameless plug for an article in the archives):

For play-calling in football, and other activities/professions where it’s vague, I think the easiest way to establish the existence of a spectrum of skill is to ask the question: is it possible to be intentionally bad at this? For me personally, this question seems to evoke a clearer response than the reverse question, even though an affirmative answer also confirms that it’s possible to intentionally good as well. For NFL play-calling, it’s really easy to picture someone who is intentionally terrible at picking plays in-game. Even if you remove “completely random” play-calling that includes non-sensical punts, kneels, and spikes by the quarterback, it’s easy to see how one could be bad at picking from a set of run and pass plays. I would personally call a “Double Pass” on every down to sink my imaginary offense.

Even in this “sensibly random” play calling regime, you likely perform far worse than a coordinator selecting random plays from a situationally-relevant subset. This type of “situationally random” performance means coordinators select randomly from plays that suit the moment. For instance, when a team is winning near the end of the game, a coach can pick a random run play to keep the clock moving, or use random pass plays if they need to make a comeback. While the play selection is still random, the type of play is limited to a subset of the total available based on the most basic aspects of the game situation. At this stage, the resemblance to the role of the actual play-caller is becoming uncanny!

With the knowledge that there exists some spectrum of skill at calling plays (thanks to the existence of my terrible trick-play-only package), we can move on to figuring out what the curve of skill-versus-impact might be for play-calling in Step Two. We’ll also try to figure out where each of the three types of random play-calling fall on this continuum!

In the “Thinking in Extremes” article, I highlighted four major curve shapes that embody most impact-related relationships. There certainly isn’t a step-change relationship between play-calling skill and impact on game outcomes (i.e. scoring points). I’d also venture that there isn’t a “power” relationship either, if only because most performance curves follow a logarithmic decay at the highest end of performance instead of an acceleration into exponential gains (as we’ve seen previously). In fact, the concept of a logarithmic relationship between play-calling skill and outcomes feels most apt to me, partially because it’s such a ubiquitous function in sports, and partially because an unbounded linear relationship between play selection and scoring points seems nearly impossible. If I magically became twice as “good” at calling plays as the best OC in the NFL, I wouldn’t expect to score significantly more points than them with the same team and set of plays.

Finally, we can complete Step Two by scaling this logarithmic curve to better represent the spread of impact compared to skill, and place our data points of interest along its path. For this part of the exercise, we’ll plot each of the three categories of randomness described above, and I’ll also toss in what I think would be the location of NFL play-callers, elite NCAA play-callers, and youth coaches (i.e. high school and peewee football).

Although the “Skill” axis cannot have a real unit associated with it, “Impact” could have a variety of options that include points against expectation, yards against expectation, yards per game vs. league mean, team wins, and so on. Despite having so many options, we’ll actually completely ignore the units on the “Impact” axis as well, in an effort to keep things simple while still enabling an estimate of the relative impact of these different play-callers.

Although there could be some haggling about where the Youth and Collegiate offensive coordinators, I do think it’s fair to say that an NFL play-caller is at least twice as strong as a “situationally” random play-caller. Even with that in mind, though, the power of the logarithmic curve really comes into play to diminish the potential advantage of having an elite play-caller. The unit-less numbers here suggest that a situationally random algorithm could perform to about 80% of the level of an NFL offensive coordinator, or said differently, an NFL offensive coordinator improves game outcomes by about 26%. Of course, that additional 20%+ matters a lot in professional sports: teams in racing sports spend tens of millions of dollars for fractional percentage points of marginal advantage over the competition.

It’s worth reiterating as well that although we’ve ended up with a graph that appears deterministic and I’m making quantitative assertions now, all the information used to create this plot is qualitative, with a sprinkle of personal speculation. Could an NFL coordinator actually be over 40% or just under 10% more effective than situational randomness? Very easily, but If we believe the underlying logic of this high level estimation process, then I think we can maintain a decent level of confidence in our guess! We just shouldn’t get too carried away and take the estimation tool as gospel.

In this short article, I’m going to abstain from even attempting to obtain a concrete quantitative answer about how an offensive coordinator compares to random play calls. This topic deserves a significant amount of research and data analysis that could probably support a couple theses or publications. However, I want to propose what I think is the most straightforward way to accomplish this task that’s worth highlighting in case any reader has a few hundred hours or time on their hands!

Some great publications and online resources such as this publication from the Harvard Sports Analytics Collective provide a breakdown of the distribution of performance from different types of offensive plays along metrics like yards gained per play. Pair these per-play performance distributions with actual gameplay information like play clock, field position, etc. and it seems relatively straightforward to run Monte Carlo simulations to estimate the average performance of each of our different types of random play callers, generating data about their average yards per game and points per game. Of course, each individual play would warrant its own performance distribution that’s more specific (like these for different run types), but actually deriving these from real game might be easier than anticipated.

The results from the Monte Carlo simulations can be compared with the performance of NFL teams through two means. First, you could try to create an artificial “NFL Offensive Coordinator” play caller based on the tendencies of real team, and plug their play-calling algorithm into the same simulation as the random play-callers. Online sources like the unbelievable NFLfastR database could provide the quantitative base for creating the artificial NFL offensive coordinator’s play selection tendencies. The NFL Operations team also has a surprisingly great website with some pretty impressive data analyses that may contain the relevant data as well.

From there, you can do two things: you can see how your simulated average NFL OC compares to the random play-callers, and you can then use real game data and results to both validate the performance of the simulated OC and compare to the random play-callers with real data. Closing the loop in this way provides both a validation pathway for the tooling used to generate the random play-calling outputs and provides a concrete comparison point for the simulated random play callers. It sounds so simple, but probably requires a LOT of actual database management and refinement work. As with many other data mega-projects, ensuring the quality and consistency of the data used for the simulation probably requires 5-10x the time of the simulation design!

If you go through all this work yourself, please let me know! Otherwise, I’ll get around to it once I reach retirement, or try to earn enough money to buy an NFL team and install a mandatory random play calling package to run real-world comparison trials.

There’s the bell!

Earlier this month, we covered the underlying mechanics that make up the totality of home field advantage in major sports leagues. While some leagues, like the NHL and MLB, have in-game or structural league rules that provide concrete advantages for home teams, a wide-ranging set of more “organic” factors combine to give home teams an edge over their visiting opponents. These additional factors vary in type of impact and magnitude, and include everything from travel fatigue, to social pressure from fans, to disrupted physical and preparation for a game. As it turns out though, the whole set of home-favoring rules and effects don’t actually equate to a significant boost in performance for the home team (right now). Across most of the major pro sports leagues, playing at home provides barely any advantage at all:

*Note: The EPL has ties, so this Home Field Win % is normalized with ties removed— this is the ratio of home wins: home losses.

Two features of this table immediately stand out: 1) how tightly bunched home-field advantage is in all the leagues, and 2) how small that advantage is across the board. The quantity of structural rules and secondary advantages that favor home teams are numerous enough that for their collective to only yield a 1-5% boost in performance means the magnitude of their individual impact must be quite low for these leagues. For me, two questions immediately jump to mind that we’ll now tackle in this article: how did we get here, and what happens next?

I think the former question is easier to answer, despite the fact that there are so many individual components that make up home field advantage. The ease of answering the question stems from the fact that almost every facet of home-field advantage has been degrading naturally over time with modern transportation, lodgings, technology, and teams becoming wealthier over time to take advantage of these improvements! I’d refer you back to Part 1 of this article for a think through every individual facet if you have the time, but here is a selection of some of the more dramatic changes to some of the factors that benefit home teams (historically):

• Domed stadiums for sports like the NFL have removed a major climate and weather familiarity benefit for home teams that have more experience in cold snowy weather. A climate-controlled, shaded stadium also removes the ability to impose asymmetric sideline amenities for the opposing team— and more teams keep adding domes!

• In tandem, teams are pivoting towards less dense stadiums with more “premium seating.” This means that not only are lower-income more passionate fans unable to heckle players, but it also dampens positive home crowd support and disruptive crowd noise from a pure magnitude perspective.

• The advent of high-quality air travel, and even personal team planes significantly reduces physical travel fatigue and jet lag in some cases (this one is pretty straightforward).

• It’s perhaps a bit underrated, but I do think the combination of video call technology (e.g. FaceTime) and improved hotel quality has reduced the mental and physical impacts of missing the comforts of home for traveling teams. Especially for teams with multiple consecutive days or weeks of travel, being able to sleep in nice beds and get actual FaceTime with your family must provide a huge boost in the long run compared to 50 years ago.

• Over time, teams have had improved access to sports nutrition, familiar warmup equipment, and even better visiting locker room accommodations. Part of this comes from teams more easily traveling with more “stuff” to away games, and part of it comes from imposed rules about fairness like the NFL Equity Rule, which is shockingly detailed.

• It’s a more controversial subject, but the proliferation of video replay and animated review technology (e.g. Hawk-eye) has helped remove (in some cases significant) officiating bias from many sports. It’s becoming more difficult for fans to shame refs and umps into making favorable home team calls, thankfully.

In these examples, and within the broader list, it’s difficult to pinpoint a single facet of home field advantage that has actually moved in favor of the home team in the last 50 years, as opposed to making life easier for the visiting team. However, the magnitude of changes over time definitely varies by league. For example, the development of high speed air travel isn’t as impactful for the English Premier League (EPL), since all the matches are played in the UK, most within driving distance. Similarly, indoor sports like the NBA and NHL haven’t realized any changes from the advent of domed stadiums and arenas. Overall, though, the evolution of sports business and broader technology has pushed pro leagues far away from the days of teams taking 10-hour cramped bus rides to sit in a terrible locker room to play a game late at night in an arena with belligerent, passionate fans right in their face before sleeping in a bad hotel and traveling again the next day.

If I had to play in those conditions for a full season, you can bet I would suck when I wasn’t playing at home…

Even without attempting to quantify each of these trends and effects individually, if these qualitative anecdotes and trends hold true, it’s straightforward to theorize that most leagues probably used to have much higher home win rates than they do now. In other words, it’s unlikely that home team win rates have been clustered around 50% in perpetuity given how much has changed since the inception of most leagues— home field advantage has almost certainly eroded over time.

Perhaps unsurprisingly, that supposition appears to be true across most pro sports leagues today, and might be most visible in the NHL, where home team win percentage has dropped from a peak of nearly 75% in the mid-1920’s, down to the 54% number we see in the 2010’s and today:

Beyond the fact that it’s wild to imagine a 75% home win rate in a sport today, the consistent decline of the NHL’s home team advantage over the last century is quite interesting. Despite a significant amount of variation in home team win-rate season-to-season (also intriguing), there has been persistent regression of home-rink advantage. Notably, the magnitude of variation has decreased significantly over time as well. It’s a bit of conjecture, but I think the tightening of the variation likely results from factors like the stabilization of the league size/team skill, and all of the aforementioned forces driving home-away parity towards 50%— for an apt visual representation, I think the spring holding the home team win rate closer to 50% is more taut than before, so small variations in force don’t move it quite as far.

We even see the same relative decrease in year-over-year home team win rate magnitude changes in the MLB, but with less overall erosion over time:

Somewhat surprisingly, MLB’s home win rate has stayed relatively steady over time, with a 10-year moving average not deviating too far from ~54%. If you squint hard, you might be able to spot a minor decline over time since the 1920’s, but not nearly on the magnitude of the NHL’s, and a much lower magnitude than I would have predicted, especially given the fact that until recently the MLB’s designated hitter rule was one of the most home-favored rules in all of sports. Although the variance in win rate has subsided— which, as we’re learning might simply be one of the major results of home-away parity improvement— the difference between the MLB’s and NHL’s trends over time must speak to some fundamental difference in the sports’ history, their interaction with travel, and the games themselves, given that both leagues have maintained similar geographic distribution of teams.

In contrast to the MLB, the EPL demonstrates a set of trends very similar to the NHL, with a consistent decrease in home win rate over time:

What stands out most to me for the EPL is how well home advantage actually correlates with all of the lower tiers of English soccer as well. The minor leagues have access to very different qualities of resources, ranging from travel modes to accommodations, although these drawbacks to away matches might be offset by a much lower home fan participation in the games. The EPL also stands out because it has the lowest travel burden of any teams we’ve studied— players and coaches can practically spend every night in a season at home, which removes a huge swath of away team win rate improvement over time. All of the other factors still circumvent this to yield a dramatic decrease in home team performance over the last century. Contrary to the MLB and NHL, though, the YoY variance in home team win rate hasn’t subsided dramatically, which might indicate soccer’s underlying luck-dominance compared to other sports— chance drives more outcomes than other professional sports.

Beyond the overall trends in each major sports league (which all generally show degrading and/or low current home win rate), an endless amount of splintering home/away topics deserve their own research. Everything from isolating home field advantage based on distance traveled by the opponent and the difference of home field advantage during the playoffs (which is likely greater than the regular season) to an analysis of neutral ground games all deserve their own analyses, or maybe even their own theses. The depth of this topic is practically bottomless once you start looking below the surface.

Something that I stumbled upon very quickly in research for this article was the massive variation in relative home-game performance by teams in the NFL specifically. Not only is there a major difference throughout the league during any given period, but individual franchises also realize major swings decade-to-decade in how effective they are at winning home games and away games (switch pages in the table below for the 2nd half of the league):

Even in the MLB, which has maintained a more consistent home win rate over time, persistent variation in performance at home vs. on the road exists, though not as much as the NFL. For example, the Astros have a ~12% difference in their home win rate vs. away win rate throughout their entire history as a club, while the Mariners only have a 7% delta, despite the fact they need to travel far distances to play almost every other team. While most other teams fall between these bounds, the Colorado Rockies stand out on this chart once you notice them, as they have a franchise home win rate around 55% and an away win rate of ~39%. They are the only team to have any home/away win percentage below 40%, and have a massive 16% performance improvement at home, maybe due to altitude alone!

It’s also fun to search out examples of sports where a degradation of away team performance over time does not seem to have taken hold. One such sport is actually college football, which has seen a rapid degradation in home team advantage since COVID. Thanks to the research of one very dedicated Redditor (thank you u/FiveTickDelay), data suggests that collegiate football in the US still realizes a significant home field advantage. In the top tier of Division I football, the Football Bowl Subdivision (FBS), home teams still enjoyed a 58% win rate, an average improvement of 17%(!) from their home records when playing other FBS teams.

Despite the fact that college football is rapidly modernizing and garnering increased funding, teams now travel further than before to play one another, and the accommodations are not nearly as good as a pro sports team. Combined with more rabid fanbases and players that might not be as good at playing in hostile environments, you end up with a current FBS landscape that still favors home teams more heavily than the same sport played at a professional level in the NFL. I will be fascinated to see how these two leagues compare in the future given influx of capital into collegiate sports, so I’ll have to set a reminder for five years from now to run these numbers again!

This thread transitions well into the second question that we posed ~15 paragraphs ago: What happens next? As opposed to retrospective data analysis, this question is much more difficult to answer, at least in terms of actually predicting what will happen, but at the very least we can try to predict whether current parity-driving trends will continue and whether or not team/league incentives will do anything to slow or reverse them.

I do think it’s pretty clear that current trends in forces outside the control of leagues will continue: air travel improvements, communication technology, teams’ ability to travel with critical gear/nutrition, and accommodations will all keep improving regardless of what leagues do. For better or worse, some forces under the control of teams and leagues will continue in the short-term as well: arena designs with smaller/wealthier crowds and climate-controlled environments, improved officiating, and increased parity in warmup/training capabilities will continue to make away teams more competitive with teams at home. Some secondary effects like increased parity in team skill and an erosion in individual team fandom due to sports gambling and fantasy sports (my own hunch), may help drive home team advantage out of most professional sports. Especially if teams begin to utilize supersonic jets once they become available again, as predicted, maybe the only team in most pro leagues with any home field advantage will be those in Denver and Salt Lake City due to their altitude!

If this sounds like a relatively bleak future— one that doesn’t reward in-person fandom with improved team performance— I agree. The silver lining is that I also think that most professional sports leagues and teams themselves would agree as well. They want to incentivize fans to attend games, and a major way to do that is to have them feel like part of the team, and that they can influence the outcomes of games (to a limit). Without some sort of concrete rule or incentive-based system that favors home teams, most leagues may naturally end up at full home/away parity, which I think will naturally breed changes if it does occur, though their shape is very difficult to predict well given how complicated and controversial implementation can be.

I could easily see leagues tinkering with the value of home/away wins, or adding additional rules similar to the NHL’s “last substitution” rule that actually affect the gameplay in favor of the home team. How this could look in practice across the wide variety of pro leagues is certainly up for debate, but I think having the home NFL team pick whether to kick or receive to start the game instead of flipping a coin, or permitting home teams to actually leave their opponents with worse operating conditions during a game could both be on the table. Maybe NBA teams should simply adopt the “last sub” rule from the NHL to slightly favor home teams!

I also think that individual teams should try take their destinies into their own hands a bit more. They should, within allowable limits, make the lives of opposing players and coaches more difficult. In an “ideal” world, teams would force their opponents into worse sideline accommodations, more unique weather/wind environments, and use extremely loud noise to disrupt their flow. I personally would not be above using artificial wind, heat, cold, or sound to throw off an opponent (and rumors have swirled about teams doing it in the past). Contrary to current trends, I also think that teams should lean further into higher-density outdoor stadiums if they want to stand a chance of maintaining a unique environment. Teams for outdoor sports should remove domes and lean into their extremely cold or humid home climate, and even indoor arenas could be heated or cooled to favor the home players.

Despite my own optimism (or delusion) that teams and leagues may adopt some of these asymmetric rules and structures, it’s worth repeating that they may be extremely unpopular with different stakeholders due to fairness, team comfort, or even financial reasons. It’s clear that without any intentional steps by professional sports leagues, home teams will eventually hold close to zero advantage over their visiting opponents, which is a lose-lose-lose for leagues, teams, and most importantly fans. It will be fascinating to watch this journey unfold, and I expect we’ll see very different mitigation tactics from individual leagues that will be extremely fun to follow.

No matter what happens, at least you’ll have some good sports-related water cooler factoids to bring back to work in the new year!

I uncovered these statistics in research recently and aside from some deeper questions about how to prevent these rampant ACL tears, I was left even more fundamental biomechanics 101 questions:

1. What even is an ACL?

2. Actually, what is a ligament?

3. Why do NFL players keep tearing them?

Thankfully, with the power of the internet, we can answer these with all the information of subject matter expert channeled through a non-subject matter expert (yours truly)!

The ACL is one of the four major ligaments in the knee that connect the femur to the shin bones— the tibia and fibula. The collection of these four ligaments provides linear and torsional stability to the lower leg which is clearly important for high performance sport! More specifically, the ACL originates on the lateral (“outside”) of the bottom of the femur, and connects to the tibia diagonally, to the “intercondylar” region close to the center of its top. This orientation allows the ACL to restrain “anterior translation” of the tibia relative to the femur— effectively the tibia moving towards the center of the body relative to the femur— and control rotational loads. It’s essential for stability when performing any lateral or turning movements including cutting or pivoting, and in extreme cases can hamper leg alignment during walking.

That’s what the ACL does, but what is it? My biomechanics professor in grad school once described ligaments in very basic terms: they are highly inelastic tissue that connect other “like-to-like” tissues— so bones. This stands in contract to tendons which connect “like-to-dislike” tissues, namely bones and muscles. While muscles can actively change shape/stretch/flex, ligaments (and tendons) are composed of much denser fibrous (primarily collagen) tissue that resist deformation under tension. Unfortunately, many ligaments are dense enough and isolated within the body to an extent that they don’t receive enough blood flow to heal quickly on their own. While minor tears and stretching (collectively “strains”) can recover on their own, complete tears often require surgical intervention to repair or reattach a ligament.

Unfortunately, the ACL is strong enough to prevent strains and tears in “normal” use, but far from unbreakable. Ultimate ligament tensile strength varies by person and between different ligaments within the body. The ACL specifically has been shown to withstand forces up to ~2,000 Newtons of force before failing, roughly 450 pounds. Its elastic (non-injurious) strain limit is generally around 4–6% before plastic deformation and injury occurs. Achieving a torsional or lateral load of 450 pounds on the knee is very difficult to achieve for most people, but it’s also a load that can easily be achieved during extreme events like ski crashes, and can be achieved by very high-performing athletes who are actually able to impart multiple times their body weight of force into the ground. Add on the fact that all ligaments are much weaker when non-linear forces are induced, and you have a recipe for disaster for sports like football.

ACL tears occur very frequently in the NFL specifically because the sport routinely subjects the knee to extreme linear tension, often with simultaneous loads of rotation and shear. This nasty combination of forces on the ACL occurs for two main reasons. First, it’s easy to see how players falling on each other’s legs in piles could cause the knee rotation shown in the image above when the foot is planted. Especially when players are making lateral movements, the outside of their leg is exposed, and if someone falls on it, or tackles from the outside, it can easily lead to a knee ligament injury.

Second (and more interestingly), players like wide receivers and running backs are constantly executing high-speed cutting and decelerating turns that generate large internal and external torques as players rapidly change direction. Once again, this requires a fully planted foot with cleats gripping the turf, creating a powerful “twist-and-shift” mechanism that drives the tibia forward or inward relative to the femur. Often, these injuries are called “non-contact injuries.” Add to both of these tearing mechanisms the sheer mass and acceleration of elite athletes—often exceeding several thousand newtons of joint load during peak maneuvers—and the result is that even a small misalignment or unexpected perturbation can overload the ligament’s narrow elastic window, causing it to rupture.

Now that we’ve answered the three core background questions, we can finally turn our attention back to the future. In particular, we can try to figure out how to decrease the rate at which ACLs are torn in the NFL (a simple task).

Up front, I do think that mitigating most ACL tears that occur when a player tackles or falls on the exposed leg of a competitor will be difficult. Similarly, I think any propositions that request players actually get slower/weaker will be met with skepticism or flat denial. Of course, the easiest way to ensure that a player doesn’t transfer extremely high loads through their ACL is to minimize the amount of strength that the athlete has in the first place For instance, I don’t even know if I’m physically strong enough to possibly rupture or even strain my ACL without some sort of external force applied. Unfortunately, pro athletes will always be incentivized to maximize strength and speed, even if it means they do become physically able to damage their ligaments with their own strength alone.

On the other hand, I think there is a LOT of opportunity to make progress on the issue by modifying the actual interface between the player and the ground. Particularly for non-contact injuries, the degree to which a players’ foot is planted firmly in the ground matters just as much as their ability to impart force through their leg— in fact having a firm connection between the foot and the ground actually enables high energy transfer into the knee. The logical subsequent thought, then, is that the NFL should make sure that players aren’t actually playing on firm, grippy ground OR they should be limit the size/depth of cleats that players use.

The evidence, though mixed, suggests there may be a higher risk of ACL and other lower-extremity injuries when playing on artificial turf (engineered to be grippy) rather than natural grass. For example, a recent analysis of 2021–2022 NFL seasons found that lower-extremity injuries occurred at a higher rate on artificial turf (1.42 injuries per game) than on grass (1.22 injuries per game), and the odds of requiring “season-ending” surgery were significantly higher on turf (odds ratio ~ 1.60). A more recent 2025 study found that across a wider variety of sports, players experienced a 67% higher rate of ACL sprains on a popular artificial turf surface brands compared with natural grass surfaces. However, this study showed inconclusive evidence for the NFL specifically, in part because the sample size is so low for full (more public) ACL tears, and teams often keep minor strain information more close to the vest in terms of specific and accurate reporting.

So while it’s possible that switching from turf to grass for all fields could yield a reduction of ACL injuries, I don’t think the NFL is thinking about more extreme solutions enough yet. Despite having limited (i.e. no) evidence, I’m fairly confident that the NFL must see far fewer ACL injuries specifically during both heavy rain and snow games. Players often use larger cleats to compensate for the more slippery surface, but even still, the chances of being able to get a firm enough grip on the field to get enough force through the knee to damage the ACL are likely low. Of course, slipping and sliding around a field yields its own potential hazards like groin tears and much poorer play by the teams themselves, but I genuinely think something akin to an actively watered field to increase its slipperiness could significantly reduce the quantity of ACL injuries.

Plus, muscles like the groin can repair themselves over time without surgical intervention, and playing in a muddier slick field could also make other impacts like tackles a bit lighter on the players too!

There’s the bell!

During Week 7 of this year’s NFL season, the New England Patriots made a trip down to New Orleans to face off against a struggling Saints team in what many called a “trap game.” The Patriots were looking strong and coming off a string of wins that might have them overlook the strength of the Saints. Well, that oversight might occur it weren’t for the constant media talk about how the Superdome is an extremely difficult place to win games as the away team, an opinion shared by so many that the Saints organization itself once wrote a blog article about it. Apparently heeding the warnings, the traveling Patriots went all out and won a relatively comfortable (in my opinion) 25-19 victory over the home team despite all the chatter, and the Saints now sit with a 1-5 Home record and a 1-5 Away record at the time of writing.

What gives? I thought home field advantage used to mean something, but now, maybe it doesn’t? Well, despite my own tendency to jump to conclusions quickly, maybe one anecdote that includes my favorite NFL team doesn’t actually tell the full story. From a first principles perspective, it’s easy to imagine that home field advantage could still be a persistent phenomenon, even in modern times. Tangible secondary benefits like lack of travel, familiar facilities and playing fields, and more psychological benefits for home players exist. On top of this, most sports actually have explicit rules that favor the home team. A review of the landscape of the “Big Four” sports in the US + the English Premier League (EPL) highlighted how pervasive these concrete benefits are, though with varying levels of effect:

Even if I’m missing some minor home-team favoring scheduling or in-game rules (please let me know!), the NHL stands out on this list as tilting the winning odds in favor of the home team the most since the NL added the DH in the MLB. Some leagues like the NFL and EPL give home teams a more nuanced logistical advantages like selecting game balls, uniform color, official timekeepers, the “chain gang,” but nothing quite matches a home team’s ability to change lines after the away team, or to pick whether to lead or finish a shootout in hockey. The EPL adopted the standard European soccer practice of limiting away fans to a specific small section in the arena, which definitely can have a mental effect on players, but isn’t as directly impactful. As much as I do love the idea that an NFL team could decide to open their dome to hellish weather 90 minutes before game time to completely throw off an opponent, most of the remaining factors that compose the structural home field advantage come from secondary, “non-regulated” elements.

When you set aside any potential corruption or home team officiating bias (the latter of which can definitely still happen), the totality of home field advantage effectively boils down to the sum of factors that variably impact a player’s or coach’s physical or mental ability to perform at the highest level based on where and when the game location. With some factors, the impact is a positive one on the home team, in others, a negative impact on the away team, and in many cases, it’s both.

If you go one level deeper than a simple physical/mental delineation of factors, you can come up with a wide variety of ways to classify home field advantage factors. However, I think the following buckets do a good job of describing factors in a (mostly) mutually-exclusive and collectively exhaustive manner and only somewhat look like freshman required seminar options:

• Architectural

• Environmental

• Infrastructural (a real word)

• Logistical

• Social

I think every factor affecting home field advantage that I can highlight can be uniquely classified in one of these intentionally vague categories, along with a physical/mental impact. To help illustrate through a cherry-picked example, an away team locker room that doesn’t offer players the right equipment to warm up pre-game would be an infrastructural-physical disadvantage, while a locker room that is so small and dingy that it just grates on the away team’s happiness would be an infrastructural-mental disadvantage. Of course, there is a natural blending of physical and mental impacts, but I’ve done my best to think of as many unique example factors as possible (similar to the numerous luck/skill drivers from part one of that series), and catalog them in a single bucket in the table below for your review (please let me know if I missed any!):

Notably, I could also only think of one potential advantage for the away team, against a veritable laundry list of benefits for the home team. In total, I tabulated 20 game preparation and execution factors that could vary for home teams and away teams. My rough ranking on impact should be taken with somewhat of a grain of salt because extreme versions of each factor could theoretically dwarf the rest, but I tried to collapse my estimate of the relative average impact in the modern pro team sport landscape to a single value. Among this list of potential home-favored impacts, a few fun “extreme” examples in the world of sports stick out.

I personally believe that playing field dimensions have the biggest impact on game outcomes, and although many leagues like the NFL, NHL, and NBA all have regulation playing surface regulations, different shaped soccer fields and especially baseball fields can significantly favor one team or another. Golf isn’t a team sport, but is the most extreme version of familiarity with playing field architecture. Interestingly, international hockey traditionally has an “Olympic” size rink that favors certain types of players (although Milan might not have their smaller-than-NHL rink ready for the Olympics in a month). Standardization of playing field in most leagues is great, but I do wish leagues allowed for some fraction of the creativity that you see in baseball!

While stadium altitude only matters in major US sports in cities like Denver and Salt Lake (and doesn’t matter at all in the EPL), fatigue and related effects from long-distance travel can have a large cumulative impact. Particularly as it relates to sleep quality and physical preparation for games, travel and playing at abnormal times can significantly alter performance (smelling salts can only take you so far). This effect is, of course, more prevalent in major North American leagues where travel can span from the west coast to the east coast (or into Europe for the NFL and NHL). I also feel like these effects must be far worse for the California universities that for some reason opted to play in the Atlantic Coast Conference the past couple years… (I still don’t understand this decision)

Of course, what home field advantage is really known for is the boost of increased fan support. The aforementioned New Orleans fans have been known to disrupt opponent play calls by being so loud, and certainly the ratio-controlled crowds at EPL games can boost the morale of the home team significantly. In addition, for sports like the NBA where players come within earshot of fans, it’s becoming increasingly common to have individual fans throw off the flow of players and coaches. However, I don’t think anything will ever compare to the old crowd in Madison Square Garden heckling and grabbing the Bruins players to the point that they ran into the stands and fought fans (worth a watch if you’ve never seen it, it’s crazy).

Given all these secondary factors bolstering the many rules that structurally favor home teams, most leagues must have a relatively strong signal for home field advantage today, right? Well… not so much. It turns out some of the biggest sports leagues have very little existing advantage for home teams:

*Note: The EPL has ties, so this Home Field Win % is normalized with ties removed. For both the EPL and NHL, you often see a “home point rate” or a simple “points per game home/away” to compare more completely, though this normalized value still tells an interesting story.

I’ve somewhat buried the lede here by building up the potential benefits of playing in a home arena, but despite having viewed this data for many hours now, it still surprises me 1) How tightly bunched all the leagues are in their recent seasons, and 2) How small the home field advantage is in every these leagues. The quantity of structural rules and secondary advantages that favor home teams are numerous enough that for their collective to only yield a 1-5% boost in performance means the magnitude of their impact must be quite low individually and in concert. As someone who loves the idea of heading into TD Garden to give the Bruins or Celtics a massive boost against a visiting opponent, it’s honestly kind of demoralizing to know we’re only moving the needle by a few percentage points or less, considering the non-fan factors!

I will say that the one facet above that did not surprise me is that the EPL has the lowest home team win %. Newcastle United will travel the farthest for their away matches this season, and all 19 fixtures amount to ~3,400 miles of total travel. Total distance traveled isn’t the only factor that leads to fatigue, but the LA Rams traveled from Los Angeles all the way to London for a game earlier this year and covered ~5,500 miles on their way there. EPL teams also don’t have to deal with changing time zones or extreme differences in climate, which help mitigate additional home field advantages, even if limitations on away crowd size exist. Although I would have thought that the MLB would have greater home field advantage due to its variable stadium/field setups, it’s not surprising to me that the major sports in the North America are clustered close together in terms of home field advantage— they are all spread geographically enough with relatively similar standards for consistency in playing field and visiting team accommodations.

Inspired by the impending release of the new Knives Out film on Netflix, I think I’d likely declare home field advantage in the current era of major sports “dead,” and would like to begin questioning. I have two initial inquiries (in an unplaceable southern accent). First, when and how did this home field advantage we’ve all heard so much about get murdered? Second, now that it’s gone, what do we do know?

With that extremely convoluted teaser out of the way, see you for the next installment!

Of course, I couldn’t help but generate my own list of “Good Gear” again this year, partially because I have no self restraint, and partially because I think it’s useful to see a wide range of gear recommendations for items that work well, last a long time, or both. As with last year, some of the things on this list can likely only be purchased used at this point since they’re a few years old, but shopping for used sports equipment is never a bad bet, especially for durable items like skis and bikes— maybe less so for sports nutrition products… Regardless, hopefully you enjoy this rundown on some of my favorite gear from 2025, and maybe it’ll spark either some good gift ideas for the holidays or a debilitating Ebay addiction hunting for 2nd hand sports equipment.

On to even more gear recommendations, complete with a selection of related bird photos throughout the year.

Cold

Snow Pants | Mammut Taiss Pro HS Pants (2024)

Annika and I both got a pair of these Mammut beasts last year after seeing them in a Mammut store in Zermatt. Technically they aren’t pure snow pants, but they’re made for winter mountaineering so are waterproof, robust and the full side zip is unbelievable for touring and uphill. They definitely are not the warmest snow pants with no liner and a thin shell, but I think they’re tough to beat for a high output winter activity.

Prescription Goggle Insert | SportRX Prescription Insert (2024)

I finally bit the bullet this year and got myself a pair of prescription goggle inserts (after needing them for probably a full decade). I use my SportRX inserts with my Smith M4 Mag goggles and haven’t yet had any issues with fogging, plus I can finally see the great views and hazards in front of me! If you don’t like using contacts but want to see while skiing, “look” no further.

Light Insulated Jacket | Black Diamond First Light (2024)

The Black Diamond store in Boulder had this lightweight puffer on sale last year because they had to roll out a new version that used fewer PFAs materials. I’m sure the updated, more environmentally-friendly version is great as well if you’re looking for a puffy coat comfortable down to the mid-30’s when walking around, and mid-10’s when working out. The black color also flexes well as a slightly more formal looking work-friendly outer layer over a collared shirt if needed.

Winter Shell | Mammut Stoney HS Hooded Jacket (2024)

Alongside the Taiss Pro HS pants and the First Light puffer, this completes my revamped skiing kit. It’s a great flexible hardshell that does run slightly narrow across the chest, so it’s worth trying on before you buy one. Otherwise, no notes!

Hot

Trail Runners | Nike Zegama 2 (2024)

The Nike Zegama 2’s might be the most stable and capable all-around trail runner I’ve used in recent years. The lugs are large and grippy, and the wide base gives you confidence on any terrain. These survived 5 long trail races for me in the past calendar year, and my only gripe is that the outsole comes unglued from the midsole in the heel over time. This can be fixed with Gorilla Glue though!

Road Runners | Hoka Mach 6 (2025)

Unfortunately I had to transition away from the now-sunset Saucony Endorphin Speed 3’s this year after my pair wore out. The Hoka Mach 6’s have become my workhorse for all my long runs and workouts this year, but I do think I may end up changing to a new model next year. These are GREAT shoes and a no-brainer if you want one shoe to do it all decently well, but I think they are a little stiff for my taste.

Shorts | Norrona Senja Flex 9” (2024)

As with last year, I don’t know that I would buy these at full price for $109, but I would buy them again on sale in a heartbeat. I’ve come to appreciate the trail- and ultra-focused features and pockets even more this year.

Sunglasses | ROKA Torino 2.0 and ROKA Oslo 2.0 :(

Unfortunately I lost my pair of prescription Torinos during the Baldface Scramble race earlier this year. You can even see the glasses still on top of my head in the official mid-race photos, but they fell off without my knowledge on a downhill climb. The Oslos replaced the Torinos, but both are great glasses. The Torinos are slightly heavier and better for biking and the Oslos are lighter and better for running/relaxing.

Nutrition & Hydration

Water Bottle | Salomon ACTIVE Hand Bottle

I was torn between this Salomon bottle and the Hydrapak SkyFlask Speed 500ML. I have one of each, and each has very clear pros and cons. The ACTIVE bottle has by far the more comfortable hand strap, but the Hydrapak has the combo screw/flip top that’s much more convenient for racing. For daily use, definitely go with the Salomon!

Nutrition | Science in Sport Go Isotonic Energy Gel

This is a great don’t-need-water gel that tastes decent! Not much else to say— I think most energy gel favorites come down to personal preference.

Tech

Workout Headphones | Aftershockz Open Run (2025)

Once again this year, if I had to recommend a single piece of gear from this list, this would be it. These are the best headphones (in my opinion) for road cycling and running when you need awareness of your surroundings. The battery is great even in the cold and the sound has been slightly improved from older versions. Mine fell in the toilet and still survived too, for whatever that’s worth.

Smart Watch | Coros Pace Pro (2025)

Last year I transitioned from the Apple Watch ecosystem to the Coros ecosystem, and I couldn’t be happier, particularly for the hardware upgrade. The Pace Pro has 24+ hours of activity battery life, and I usually charge it once a week with 4-6 workouts/week. The integration with Apple Health is pretty good (for now), and the Coros proprietary software is pretty strong. For the better battery life alone, the switch is worth it, and I think the Pace Pro is the best option for most people.

App | Find My Friends

I use this to track Annika during her trail races and at marathons. It’s such a game changer for meeting people at aid stations and coordinating plans through crowds at the end of races.

Bike Trainer | Wahoo Kickr (2020)

I still think I would purchase a different trainer today (like the Kickr CORE), but this is a great piece of sports equipment! If you’re looking for a bike trainer, I don’t think you can go wrong in the modern name-brand market.

Some of the topics included in the report cards themselves, like quality of food and dining, seem pretty superfluous on the surface. However, I do think the variety of topics do provide a Glassdoor-esque level of insight into a lot of the day-to-day facets that make a football team a good or bad place to work. I’m not entirely sure how you assign some of the categories a specific letter grade, but the overall quality of a team’s locker room, travel accommodations, and training room are all facets of a pro sports workplace that are useful to understand. Does it feel a little mean that the players publicly grade their dietician, training staff, strength coaches, and team management? Probably yes, especially because there are likely very few (or one) person in these roles.

I think it’s also worth noting that each of the items on this report card shouldn’t be weighted equally when it comes to providing each team with an overall single grade/ranking. In general, the quality of the team-provided meals and dining area is likely far less impactful for the players and a teams’ outcomes than the quality of the head coach or the ownership (even if the ownership controls those facets of the team). However, when you flip through the 2025 team report cards, there are some extremely poor grades in seemingly mundane categories that make you raise an eyebrow about why any player would join that team at all.

For example, the Cincinnati Bengals received an F- in Treatment of Families. What does that even mean— how is that possible? Is the owner actively blocking players’ kids from getting into preschools, or preventing anyone from getting health insurance or something crazy? Similarly, the Patriots and Eagles both got an F for Team Travel, and questions abound about how that’s possible post-invention of the airplane.

Despite chuckling at all the weird grade anomalies in the owner-panned 2025 score cards upon first read, two thoughts entered my brain. One, I can see why the NFL owners want to keep this information from making it to the public: it’s a very strong way for players as a group to drive change within their organization and the league more broadly. Being slightly tongue-in-cheek, if I was a player, I’d also ask to grade more specific traits of the workplace so it focuses on niche areas I’d like to see improvement, like “On-site Movie Theater Quality,” or “Cereal Selection,” or “Door-to-door helicopter service,” among the other job perks that really help impact a team. Far more importantly, though, I really believed that the overall grade a team received must be correlated with their success.

In all jobs, but especially in professional sports, success begets happiness and fulfillment. Even when you’re trying to be as objective as possible, if you’re company is really successful and you like your job, your answers about the quality of different workplace facets would likely be inflated, OR you could trick yourself into thinking the low quality of something was actually a key to your success and reverse-psychology your way into a high grade. As it turns out, though, throughout the three years of data provided by the NFLPA, there’s effectively zero correlation between a team’s success and its ranking among the 32 teams in the league as a function of the average of its letter grades. The scatter plot below shows the number of regular season wins for each team over the last three seasons and their resulting rank among the NFLPA report cards, and it looks almost like a perfectly random distribution:

The randomness of this distribution comes into even clearer focus when you look at a line plot where each data point represents the Average number of wins, and Average team grade over the last three seasons. If these two variables were correlated, we’d likely see some sort of semi-linear line from the top left of the frame to the bottom right. Instead, we got this completely disorganized plot that looks like it could be the result of my trying to draw a camel on a whiteboard while blindfolded:

In short, it seems like my proposition that NFLPA Team Report Cards are correlated with each team’s success is incorrect (at least for now). Of course, we don’t have too much data gathered yet, and it’s very likely we never will if the owners are successful in blocking future grades by the NFLPA. Pending more information, this result still surprises me, a reaction that’s bolstered by the fact that I couldn’t find a single category that strongly correlates to winning in the regular season— not even coaching or ownership grades! As an anecdote, the Miami Dolphins rank nearly at the top in every category this year, and (respectfully) they kind of suck at the same time: a very counterintuitive result of the team report cards.

The implications of this data-light result are quite funny. While the information about specific aspects of each team’s workplace may help influence players’ decisions about where to go over time, there’s currently no reason to believe a team with more reputable grading will perform better. For players looking to win, maybe the grades don’t actually matter then, and more importantly, owners shouldn’t really worry about the results of the grading either, at least in the short term. Obviously all top companies want to use every job perk and lever to attract star employees, but the initial data suggests it might not matter given the variability and wide range of outcomes in the NFL specifically— teams can still succeed without high grades in most areas. The New Orleans Saints may be doomed to have D- Food/Dining in perpetuity!

Despite the questionable usefulness of the data produced by the NFLPA, I do hope that other leagues with higher skill expression and more deterministic success patterns someday copy the NFLPA and produce team scorecards so we can gather more data on this subject. Maybe then my immediate conclusions that winning and grades should be correlated will just be early instead of wrong…

There’s the bell!

Like Cam, I’m afflicted by a chronic obsession with outdoor gear. In grad school, one of our ideal dates was walking along Boulder’s Pearl Street, stopping by each outdoor apparel shop, and taking notes on all of the commonalities and differences we were seeing between brands, and what types of ‘layering systems’ we thought would be appropriate our activities. (You know you’re in deep when you call a bunch of clothing a “system”)

So this year, I wanted to jump in and give my two cents about the best gear, workouts, and nutrition I used in 2025. Without further ado, my favorites!

Gear

Road Shoes | Nike Zoom Fly 6 (2025)

Starting out strong with my MVP shoe of the year. I’ll be the first to admit that usually, Nike shoes don’t work for me. I’m a wide foot gal, and for the past few years, the Saucony Endorphin Speeds have been my go-to all-around marathon trainer. But as is the way with most favorite shoes, Saucony’s 2025 update to shoe resulted in an entirely different toe box shape, which unfortunately left me and my spread-out toes out to dry. In my search for a suitable replacement, I was drawn in like a kid at a candy shop to the Zoom Fly’s beautiful colorways. Deciding to take a chance, I sized up a half size (which I usually do in race and carbon-plated shoes, pro tip) and voile! Not only do these look fly (hehe), but they are an incredibly versatile training shoe, which accompanied me on everything from speed work to my long runs with some tempo work at marathon pace. The lesson? Maybe don’t count out any brand, as they may release something that surprises you!

Trail Shoes | Hoka Tecton X 2 (2025)

Next up is another shoe. This summer, I had quite a few ‘runnable’ trail races on the calendar, and was looking for something that had a little more pop than my usual GOATs, the Hoka Speedgoats. My search led me, frugally, to Hoka’s summer sale section, which miraculously included the Hoka Tecton X 2. Deciding it was worth the risk, I purchased these carbon trail shoes in a groovy ‘stealth’ colorway in my usual size, and have had no regrets. I find them to be just as comfortable as the Speedgoats (no blisters on anything < 50 miles) and with a slightly smaller platform, they keep me nimble on roots and rocks when fatigue sets in later in a race.

Snow Pants | Mammut Taiss Pro Pants (2024)

Ah, snow pants. If you’re anything like me and my quad- and glute-dominant rower friends, you know the pain of trying and failing to find the perfect snow pants that actually stay up during all of your winter activities. I had resigned myself to a life of athletic belts or suspenders, until I tried on these beauties in a rare encounter at a Mammut store. Turns out, my new favorite snowpants aren’t actually marketed as snow pants at all! Instead, these ‘alpine pursuit’ pants are designed as a jack of all trades, made for everything from ice climbing to hiking. Tragically, given their price point, they are also just…better than anything else I had ever tried. (Sorry, Patagonia!) With an adjustable waist belt and perfect fit around my ski boots, with textured cuffs that keep the snow from sticking to your legs, these pants are my perfect backcountry ski gear. They’re lightweight, unlined, and most importantly, feature a full-length side zip to let your legs breathe during big uphill efforts. 10/10, would pay full price for these.

Light Jacket | Patagonia Nano Puff Hoodie (2025)

This one is a late addition to my list. I had been considering a Patagonia Nano Puff for several years, but always talked myself out of it on account of already owning a puffer jacket, hard shell, soft shell, sweatshirts, etc. It seemed like a fan favorite, but I wondered just how much use I would get out of adding yet another jacket to my already robust jacket collection. Lucky for me, Cam made the choice on my behalf, and gifted me the hooded full zip for birthday this year. I promptly put this jacket to the test in the southern hemisphere’s winter during a trip to Kenya, Zimbabwe, and South Africa. Truly, I do not know what I would have brought with me if I hadn’t been given this jacket. It’s lightweight, warm, and most importantly, packable, which mattered a lot with our 25 pound bag limit on safari. In another universe, I was freezing cold and sad for the entire duration of this trip. Thank you, Nano Puff!

Running Hat | Ciele Hat (2024)

This is the part of the article where I admit that I have a conflict of interest: I am, in fact, Canadian. And so I always make out to try out Canadian brands wherever possible, especially when they infiltrate the mainstream gear space. Ciele is a Montreal success story, and is virtually inescapable in most running groups now. Unfortunately, like the snow pants discussed above, they also sell relatively expensive products. $50 for a hat?? But I am a hat girl. I run in a hat every day, and on my longer runs, will end up sweating heavily for 6+ hours. This weekly sweat-salt-wash cycle causes a lot of wear and tear on my daily gear, but even after thousands of miles, these hats are going strong. They also come in a lot of cool colors, which as you can probably tell, I’m a sucker for.

Running Hat 2 | Parapack Black Folding Hat (2024)

Forgive me for including two hats on this roundup, but this one was too unique to pass up. I received this black, cinchable folding hat from my brother-in-law last Christmas, after he discovered it at a San Francisco market from Parapack. While not as robust as the Ciele hats, it packs down to almost nothing, and is a great addition to your race gear if you’re looking for something small and lightweight to break out during daytime stretches.

Headphones | Shokz OpenRun Pro 2 (2025)

I’ve been a Shokz fan for almost 6 years now. As I’ve mentioned, I’m an extremely salty sweater—to the point that I’m no longer embarrassed to admit that I’ve killed more than three different types of sports headphones from my sweat alone. Whether on the trail or on the road, I like being able to hear folks coming up behind me, and I’ve found the Shokz to not only be robust to water (I literally left these outside overnight in the rain and they were totally fine) but they also provide great sound quality while maintaining situation awareness during workouts. I like that they are connected—so you can drape them around your neck—and that they sit on the ear, rather than inside.

Specifically, I also want to highlight OpenRun Pro 2’s recent adoption of USB-C charging, which has finally reduced my entire charger collection down to only two types of connectors. (Looking at you, Coros—the last non-USB-C holdout!)

Nutrition (Fuel/Recovery)

This past year, I feel like I really dialed in my fuel and recovery during longer efforts, which has been an absolute game changer. Regardless of how far you’re going, I believe that everyone can benefit from making sure they’re giving their body what it needs to perform and recover. So without further ado, let’s get on to my favorite snacks!

Gel | SiS Beta Fuel

First up, we have the highly engineered Science in Sport (“SiS”) Beta Fuel. I was a Maurten loyalist for years, but I find that every now and then I need to rotate products to give my palette (and bank account) a break. I was drawn to SiS (Science in Sport) due to their focus on “liquidy” gels that are designed to be taken without the aid of water (sorry, Gu). As a runner who relies primarily on liquid calories, the Beta Fuels provided an easy, one-time boost of 40 g of carbs. Plus, I like the flavors. I think fueling can be really personal, so at the end of the day, you do you—but also don’t be afraid to try out new things, because sometimes it takes a bit of gut trial (and error) to find what works.

Salt | Pickle Juice

Ah, pickle juice! On the other end of the spectrum from engineered gels, we have this humble vinegar base. I’ve always been a fan of a good old fashioned aid station pickle, but lately I’ve started carrying these little shots of pickle juice in my pack or in my drop bag, too. While it doesn’t really provide any calories, it helps me replenish salt and avoid cramps late in a race. I carried this baby 40+ miles across the Grand Canyon and back during my 2025 Rim to-rim-to-rim (“R2R2R”) effort, and it was probably the only reason I made it out of the box alive (just kidding…or am I?).

The best part? You can find them in most gas stations!

Oatmeal

Maybe an obvious one, but I want to give a shout out to oatmeal. It’s cheap, it has a great mix of carbs and fiber, and it’s super easy to make. This past year, I started taking this humble grain with chia seeds and blueberries, with a touch of peanut butter and cinnamon. Go oatmeal!

Chocolate Milk (RIP Coffee Skratch)

For years, I was a huge fan of the Skratch coffee-flavored recovery drink, which, to my dismay, they discontinued several years ago. (I literally wrote to the company asking for them to keep the product, or at least to send me whatever they had left from their warehouse. No dice.) Since then, I’ve struggled to find a good replacement. I’m not a big fan of pure protein drinks, as they always seem kind of expensive for what they are.

But this past year, during my peak weeks leading up to the Boston Marathon, I went grocery shopping hungry (bad move, I know) and returned with a half gallon of chocolate milk. For me, a glass of chocolate milk is the prefect post-long run recovery fuel to have while foam rolling or stretching, especially when nothing else seems appetizing. Plus, it helps keep the stress fractures away!

Cherry Juice

Inspired by my friend Liz, who is always trying the latest, natural performance enhancers, I picked up some cherry juice as an aid to sleep and recovery after races. Unfortunately, I don’t have any hard data on its effectiveness, but it tastes great with some seltzer and lemon juice, and it makes for a great evening mocktail!

Workouts

In the spirit of including at least one anti-consumerism section, I wanted to highlight some of my favorite things I’ve done this year rather than just purchased or used. Here are some of the workouts that I found to be most effective for me in 2025.

Composite Workouts

I’m not sure what else to call these types of workouts. I’m talking about the kind of session that brings together a little bit of everything. For me, this often looks like running the steps of the Harvard Stadium, straight into 4x800 threshold work. This was a great stimulus ahead of the Run Rabbit Run 50 mile last year, where I surprised myself by finishing 4th after enrolling myself in the ‘tortoise’ classification. I find that these workouts not only help break up the monotony of some training days, but also help simulate real race efforts at varying speeds. Some other favorites include:

• 2 x 800 + Marathon Pace (MP) Tempo + 4 x 400

• MP Tempo + long hill repeats (2 min up) + MP Tempo

• the Michigan (which, for those unacquainted, is usually 1600-1200-800-400 at 10k-5k-3k-mile pace, with 1 mile tempo ‘off’ in between—i.e. no actual standing or walking rest)

Doing Core While Reading

During intense training blocks, core is always one of the first things to slip from my schedule. However, I’m a firm believer in core strength as a vital step to injury prevention (even for runners), so this year I started working it in to my evening routine while I’m reading before bed, or answering emails before work. Whatever it takes!

Mixing It Up!

This one isn’t really free, but it can be, if you pay attention to class schedules! This year, I made a point to try to do more things, and to seek out new experiences around my city with my friends. This often took the form of free workout classes. Boxing, pilates, weight training—it isn’t always the most specific to whatever race I was training for, but it was a ton of fun! This also doesn’t have to be a class— grab a soccer ball, grab a frisbee, and get outside in a local park. I promise it is better than always working out alone, even if it isn’t “optimized.”

Routes & Races

Boston Marathon

After years of cheering on runners on Patriot’s Day, I finally qualified for the Boston Marathon via the 2024 Newport Marathon (which I would also highly recommend!). It’s hard to describe how special this race is to anyone who isn’t a runner or from the area. It is a race with ritual: each April, over twenty thousand runners board school buses downtown, and together ride out to Hopkinton, Massachusetts, where they receive sunscreen and water from facilities tailgating in their front yards. For some, it is a charity run; last year, the marathon raised $50M+ for charitable causes. For others, it is the last stop on the tour of the world’s seven (formerly six) major marathons. There isn’t a single kilometer of the course that does not have spectators, and it makes me so proud to call this place home.

Grand Canyon Rim to Rim to Rim

I’d been wanting to run the Grand Canyon Rim-to-rim-to-rim route for several years, but couldn’t quite make the trip work with my work/life calendar. Due to the extreme heat and snow on the north rim, you can really only run the route in early November and in late May. I also really did not want to do this one alone, and so trying to get a group of friends together who were a) all fit enough to run 40+ miles and climb 10k vert in a day and b) have our schedules line up for one of three weekends was probably more challenging than actually running the route itself.

Before running this trip, I had never actually seen the Grand Canyon before. People say pictures don’t really do it justice, and I completely agree. The colors, the bands of the earth, the structure of the canyon-within-the-canyon—it all is so hard to convey to someone who has not been there. There is something humbling about looking out over the vastness of space and having the confidence/delusion to think, “I’m going to run all the way across that, and return today.”

Table Mountain (South Africa)

This summer, during a family trip that was many years in the making, Cam and I got to explore some of the striking nature of Cape Town. Thanks to some spectacular guides, we learned all about the fynbos biome that is unique to the coastal mountains of South Africa. This scrubland, combined with the rolling fog and striking geology of the Apostles, makes Table Mountain an amazing hike/run/cable car trip!

Baldface Scramble

This summer, in lieu of one big ‘A’ race, I signed up for some of the White Mountain Endurance Cup races to try out a bunch of sub-marathon routes around New Hampshire. Of the set, the Baldface Scramble was my favorite! Unlike most New England mountains, which sometimes struggle to break the tree line, Baldface has a signature bare top with lots of fun scramble sections that made this route an absolute blast. And, lucky me, the race summited the mountain twice!

Mt. Lafayette (New Hampshire)

Finally, while not a race or a usual bucket list route, I wanted to highlight New Hampshire’s own Mt. Lafayette. Of the White Mountain peaks I’ve summited (not a ton, but ~20), Lafayette still reigns at the top of my list. Featuring one of my favorite ridges, a whole set of waterfalls, and beautiful views of the Franconia Notch, I would highly recommend trying to get up to this mountain when the leaves begin to turn.

The Cowboys-Packers game is one of two scorigamis achieved this season, with the other being a neighbor 41-40 score in the Bills-Ravens Week 1 matchup. In the last decade, there have been an average of ~5-6 scorigamis per season, and given how long the NFL has existed, this would be a surprisingly high rate if the scoring system in American football wasn’t so unique and varied. As opposed to most discrete score-based games like baseball, hockey, and soccer, you can score in increments of 1, 2, 3, and 6 points in the NFL through extra points, safeties/two-point conversions, field goals, and touchdowns respectively. The various scoring increments at play pair with rules set up for periodic high-scoring affairs to enable scorigami art unlike any other sport. Novel score lines still occur relatively frequently today, and the distribution of all NFL scores throughout the leagues’ history is fascinating to see:

The heatmap initially looks quick chaotic, and certainly a lot of noise permeates the chart. However, with some initial study, interesting trends already begin to emerge! You can see a lot of bright cells in the center, especially in the 13-30 range for each axis (i.e., each team’s final score), which anecdotally aligns with the scoring range I’d expect for most games. If you squint your eyes at this bright section, you can also vaguely make out a bit of a grid pattern, where each team’s scores seem to coagulate around numbers that are more easily divisible by combinations 3 and 7 specifically, so 10, 17, 20, 27, on upward. Final scores involving double-digit numbers like 32, 25, 22, or 18 are quite rare because they involve a unique blend of scoring opportunities that are difficult to assemble in one game.

As you venture away from this densely-packed hot spot, though, you start to see football’s unique scoring system come into full effect. Along the right edge of the chart are lonely blue squares representing rare, high-scoring blowouts. This is the most fruitful region for achieving scorigami in modern times because high scores are becoming relatively more common, and it’s rare for any team to achieve a score above 40, so when it happens, you only need one safety or missed extra point to land on a unique score. My favorite score from this outlier region is the far top right, when the 1940 Chicago Bears beat the Washington Redskins 73-0 (on their home field). The highest-scoring loser of all time was the Kanas City Chiefs, in their 2018 showdown with the LA Rams, with a whopping 51 points in a loss.

Perhaps the most fascinating part of the chart isn’t even necessarily what’s colored in, but what’s still blank: the remaining opportunities for scorigami! As mentioned, ample opportunity exists to achieve scoriagmi through high-scoring affairs, near the top row (shutouts), is a large sparsely-populated band. Of course no team can end a game with just one point, but it’s remarkable how few games have ended with a team finishing with 2, 4, or 5 points, even in losses. Only one team in history has ended a game with 4 points (win or loss), and we haven’t yet had a 2-2 tie! Somewhat shockingly, five games between 1923-1938 ended with a team winning 2-0, but these low-scoring affairs are becoming quite uncommon in the modern era.

For the NFL, the Scorigami grid doubles as a timeline of the league itself. In the early 20th century, scores huddled near the low-scoring upper corner, with a lot of losing teams not posting any points due to lack of parity (usually a side effect of an immature league). By the 1980s scores drifted downward and rightward as rule changes made offense more explosive and parity began to increase, the combination of which led to modern football scorigamis achieved in the the 55-40’s and 41-40’s of our pass-happy era. Yet as the chart fills in, new scorigamis continue to become increasingly rare. The phenomenon follows a mathematical decay curve: each season, there are fewer new ways to make a number. Said differently, the length of time between scorigami occurrences will continue to grow exponentially.

You could write an entire set of theses about the mathematical decay of scorigami occurrences, and at the current rate, the NFL could run out of realistic first-time scores within the next few decades (barring major rule changes). Of course, the sport still has more ways to throw a scoring curveball into the mix, so even with scorigami becoming a rarer occurrence, the NFL still has far more runway for unique scores than other major pro leagues like the NHL, NBA, MLB, and soccer. The MLB, NHL, and pro soccer all score via single integers, and most games don’t end with an enormous amount of scoring chances due to their duration and structure.

Of these leagues, the NHL may be the poster child of a league in which scorigami is nearly impossible to achieve today. In one hundred-plus years of play, nearly every reasonable scoreline has already happened, and despite the game undergoing significant rule and equipment changes, they have favored both skaters and goalies in a way that prevents scoring from exploding like has happened in football. Somewhat unbelievably, the most recent NHL scorigami occurred over 30 years ago(!) in 1993 when the Flames beat the Sharks 13-1— a long scorigami drought that I think will continue. Teams now tend to rest star players more when they are crushing an opponent (or being crushed), and the prospect of any modern NHL team giving up more than 10 goals is difficult (but not impossible) to believe. Even the simplicity of the scorigami chart in a single-integer sport like hockey highlights the lack of numerical excitement when compared to the NFL’s.

The MLB’s scorigami chart presents more forward-looking opportunities than the NHL and soccer because of the possibility for very high scoring affairs that still periodically occur in the sport. The Blue Jays (hopeful World Series winners) annihilated the Red Sox 28-5 a few years ago, and massive blowouts still can occur regularly, especially with teams now letting position players pitch when losing. Of course, the level of potential chaos isn’t as great as the NFL, but the same high-scoring opportunities for scorigami still exist. However, I think the most interesting scorigami outside the NFL rests with the NBA, despite the fact that it only has a three basic scoring increments as well (1, 2, and 3).

What makes socirgami prospects in the NBA more exciting is the very wide range of scoring possibilities. Most NBA scores tend to land in the 80-120 points per team, but teams regularly score as few as 70 points, and over 130 in the modern NBA. This wide range inherently leaves a lot of room for unique scores even with a more standard scoring increment. I would not be surprised at all if the “bubble” in the NBA scorigami heat map below continues to balloon outward from the tie line as teams achieve larger blowout in the 120+ point range. The NBA is one of the most skill-centric leagues, which means that these lopsided blowouts are more likely to occur than in sports like hockey or baseball where increased luck influence naturally decreases the odds of massive routs.

There’s so much fascinating math to be had at by digging into the world of scorigami across the NFL and every other league (we didn’t even talk about college football which is even more insane than the NFL). The concept of scorigami and how it develops over the course of a league’s history tells you so much about the rules of a sport, a league, the athletes that play, and how everything changes over time. However, I do think it’s worth mentioning that the concept of having a game that promotes ongoing scorigami isn’t everything! Nautrally, every league eventually runs out of new unique scores to achieve, even with dramatic rule changes, and I believe there’s almost no correlation between a sport’s current scorigami potential and its popularity. Soccer might be the most popular sport on the planet and I can’t imagine most established major pro leagues achieve scorigami very often anymore!

I wholeheartedly believe that I will revisit scorigami again in the future, potentially to look at the aforementioned rate of decay in achieving scorigami for each league. Until then, though, I hope this article has served as a good water cooler topic for both in-peron and remote workdays, and you end up tracking scorigami going forward! I also must apologize for not providing any actual useful information about how to fold paper into unique shapes… but maybe in 25 years when this newsletter has transformed dramatically, we can revisit that possibility.

Pretty much every single stat from the effort appropriately dubbed “States of Elevation” comes across as unfathomable, and this has to be the article in which I will use the most superlative adjectives. In addition to the 2,500+ miles of biking, Kilian ran ~630 miles, close to a marathon every single day. Unfortunately for him, that series of marathons also included the majority of the vertical distance he covered— a whopping 403,000 ft!!! He accomplished in one month what many dedicated outdoor athletes accomplish in a year, and what most normal people accomplish in a decade of activity. Perhaps the craziest part, but maybe the least surprising, is that he’s also done this type of effort before, almost exactly one year before this journey.

Last September, Kilian climbed all 82 peaks in the Alps that exceed 4,000m (~13,100’) in altitude for his “Alpine Connections” project. This trip was equally bananas, and his running/cycling journey lasted 19 days, a period through which he averaged 5 hours of sleep and featured a 32-HOUR multi-summit effort. As with the trip across the US Rockies, Kilian mentioned that he somehow felt better towards the end of the endeavor than the start, which is… simply inhuman. I legitimately think I would just die after the first two days of the effort, let alone start to feel better later into it. Every stat once again boggles the mind, and what stands out to me is the total cumulative load on his body during both of these multi-week efforts.

Aside from the grading lack of sleep that Kilian seemed to endure, I just cannot believe that he was able to keep up with the energy burn he put his body through. In his Alpine Connections documentary, Kilian mentioned he tried to rely almost exclusively on what I’d call “real foods,” otherwise known as not gels and gummies more typical for shorter endurance efforts (I say “shorter,” but this includes Iron Man events and other ultramarathons that seem short in comparison). In Alpine Connections, Kilian averaged a moving heart rate ~110 beats per minute (“BPM”), a number only this low because of his unbelievable base fitness and light frame: he’s around 5’7” and 130 lbs soaking wet. Given the similar scope, it’s probably safe to assume that Kilian maintained something similar for States of Elevation across the US, though maybe a few BPM lower with so much additional cycling.

I think it’s safe to assume that Kilian biked and/or hiked for about 16 hours per day as he linked the 72 highest continental US peaks. Across the 31 day effort, this equates to 496 hours of exercise at his light aerobic heart rate, which is likely around 105 BPM. For reference, this is probably the heart rate a healthy middle-aged person hits just walking around town, and Kilian can keep his heart rate this low while biking with some effort and power hiking up through high altitude. Multiple online calculators including Omni’s suggest that for his age and weight, Kilian was burning ~440 calories per hour while he was on the move. Assuming his cardiovascular and neuromuscular efficiency is far greater than a normal person, we can conservatively estimate he’s burning about 70% of this rate (a guess), so ~300 calories per hour on top of his base metabolism.

Over the course of a day, this means Kilian burned about 4,800 active calories, and likely must compensate for additional base metabolic load. Over the course of 31 days, Kilian burned a minimum of 149,000 calories, and likely did not gain or lose a significant amount of weight. According to Smithsonian-affiliated researchers, the average human body contains around 125,000 calories distributed through muscle, fat, organs, and bones. Unfortunately (or fortunately) for Kilian, dense fat typically stores the most energy for a human, and he doesn’t have much to provide so he could have fully evaporated his body in this effort if he was mentally strong enough (sarcasm).

In order to sustain himself, though, Kilian almost certainly had to replace all burned calories through ingestion, which presents another extremely inverse stat: Kilian likely had to consume an average of ~350-400 calories every single hour he was awake for a full month. I love eating as much as the next person, but I can’t imagine trying to consume a normal McDonald’s Cheeseburger worth of food once every hour for a month. Given the rate of consumption, it’s no wonder that Kilian leaned on whole foods with greater variety, especially because Kilian does not consume food and water regularly while running/hiking so had to make up for lost calories with bigger meals or some good old fashioned grazing on the bike, which some of our triathlon friends call the “rolling buffet.”

As with the Alpine Connections project, Kilian and his team will almost certainly release more physiology and performance data from States of Elevation in the months to come so stay tuned for even more insane stats. Kilian has mentioned many times that his goal with these types of efforts is to advance understanding of human biomechanics, psychology, and physiology by using himself as a perhaps the most capable test subject of all time. He also uses these adventures as a means of sharing his love for alpine environments, and to publicize the Kilian Jornet Foundation, which is focused on ecological and environmental research and preservation, so I want to highlight that here as well.

I cannot wait to see what Kilian’s next project will be, and I’m secretly holding out hope that he may end up pursuing a fastest-known-time (“FKT”) traverse of the Appalachian trail in the near future. Kilian is a physiological marvel, and might be at the top of my list of people to interview in the sports world. If he makes his way to New England, I will absolutely take time off work to go cheer him on and maybe get him some extra calories!

There’s the bell!

If this overly-abbreviated summary doesn’t mean anything to you, you are not alone— I wrote it and reviewed it multiple times, but it’s not a series of words and sentences I ever thought I’d type. The story is even more convoluted than my summary, and I breezed past tons of details. If you’d like to go back to the start to see the initial set of breaking news, I highly recommend listening to Pablo Torre’s entire series actually exposing the initial scandal and the variety of follow-on details linked below! In short, though, Steve Ballmer and the Clippers may get in trouble with the NBA for what amounts to a series of direct and indirect cash flows to a super star player through a company who 1) clearly lied to a lot of investors, and 2) shares the owners’ interest of keeping Kawhi in the LA area because they were set to sponsor the new Clippers stadium.

PABLO TORRE FINDS OUT

The Richest Owner, the Silent Superstar, and the Rotten Apple Tree: A PTFO Investigation

This is a very special episode of Pablo Torre Finds Out…

Read more

8 months ago · 63 likes · 10 comments · Pablo Torre

It seems like most reactions to the news have been pretty polarized (shocker), with one camp suggesting that Ballmer was unknowingly fleeced by Aspiration, and their dealings with Kawhi were indeed completely independent of his influence. The other half argues that the amount of circumstantial evidence of intentional wrong-doing is so vast that the NBA must act and punish the Clippers. It honestly sounds like a pre-NIL era scandal that might rock the NCAA Football world if it turned out Alabama boosters had paid players through a local savings bank endorsement or something goofy. For better or worse, I find myself somewhat sandwiched between these two perspectives, and am left with more questions about how this type of scenario could even happen in the first place!

As it turns out, the NBA’s collective bargaining agreement presents a relatively concrete set of rules for what constitutes a salary cap circumvention violation of this type. (Side note, I recommend skimming the NBA’s current Collective Bargaining Agreement, it’s fascinating to read and I think it was created with LaTeX which I appreciate immensely). There’s also some funny stuff in there about no player and team affiliate (mainly owners) holding more than 12.5% of the same company or holding/influencing each others’ investment decisions. However, the two things that one needs to prove to verify if a team and player circumvented the salary cap are that the players’ contract is below market value and the sponsor/endorsement deal that paid the player was over valued for the service provided— Article XII, Section 1(b). Through this narrow lens, I’d likely suggest that Kawhi and the Clippers did circumvent the salary cap, perhaps unintentionally though.

Even if these two facets of circumvention pose challenges for actually proving salary cap circumvention in arbitration or court, at least the rules themselves seem relatively concrete. Despite a relatively large gray area, they should help disincentivize players and teams from trying to circumvent the cap. The bigger challenge for the NBA, which I think forms an interesting core “nugget” of this article, stems from the fact that its star players generate more revenue off the court than any other major sport in the US. Driven by the reality of very short average playing careers, stars in all leagues are constantly incentivized to maximize their income, which naturally leads to endorsements, partnerships, and other business deals off the court. However, the ratio of off-the-field income to playing salary is much higher for elite NBA players compared to the other “Big Four” major leagues in North America:

The NBA stands out on this chart, not only for the total income magnitude of its five highest off-the-court earners, but also for the off-field earnings potential compared to playing salary of the league’s superstars. The top-five off-field earners in the NBA earn an average of 1.1x their playing salary from endorsements and business dealings, while the top five respectively in the NFL earn 0.63x, 0.45x in the MLB, and 0.42x in the NHL (which also has far lower total earnings potential). This ratio drops off extremely quickly when you move beyond the top handful of off-field earners in each sport, but in general, the NBA still tracks higher than the other sports by a significant amount. Unfortunately, this structural advantage for NBA superstars means that they more often engage in off-court endorsements and businesses that could become intentional or accidental salary cap circumvention. Especially when the absolute value of the endorsement and business deals are on the order of $10M+ per year, it’s easy to see how collusion and circumvention could become more tempting in the NBA than in a league like the NHL, where the 10th highest off-ice earner might only add ~$250k to their annual salary.

Something about the current state of the NBA lends itself to major off-field earnings by players, which is simultaneously great for players and a major challenge for the league. In many ways, I think NBA players track ahead of the curve when it comes to leveraging their own celebrity to the fullest, but accounting for every player’s private business dealings must be quite difficult when it comes to enforcing fairness as a result! Disentangling the web of incentives and payments for the league’s superstars and ownership groups would likely be a Sisyphean task. This would also be made more difficult because of the league’s ownership rules, which now allow ownership of multiple teams, and businesses dealing between both owners and players. The types of conflicts of interests that are technically allowed in the NBA are actually quite fun to brainstorm, so I put together a short list to help illustrate:

Honestly, the list above could go on forever. At the end of the day, though, my point might be this: I think whether or not Steve Ballmer, Kawhi Leonard, and the Clippers did intentionally circumvent the salary cap is somewhat irrelevant. The incentives and interests of players and ownership groups in the NBA (and other pro sports leagues) are becoming so entangled that direct cap circumvention may become impossible to prove and might not even amount to the same impact as allowable conflicts of interest under current league structures. For better or worse, the leagues themselves benefit from their players becoming influential business partners and influencers, so I wonder if this problem is already entrenched in a positive feedback loop that leads to even bigger scandals. We’ve started to toe the line with this story and others like Tom Brady partially owning the Raiders, and Messi becoming a part owner of Inter Miami. The growing bubble of potential conflicts of interest could pop at any moment.

If that does happen, I’ll be ready with my popcorn to watch from the sidelines. Until then, I’ll likely be digging back into the fascinating insights from the data in the salary-to-off-field earnings chart— I’ve only scratched the surface of the potential takeaways!

There’s the bell!

Between 2013 to 2021, Cape Cod witnessed a 1,000%+ increase in the amount of Great White detections, a combination of verified first-hand accounts and chip/beacon pings on tagged sharks. Without any additional context, this trend would be EXTREMELY alarming— I don’t think anyone would be happy about the number of sharks increasing by 10x just off their shores. It is very possible (or even likely) that the number of predators moving north up the Atlantic coast is increasing as ocean temperatures rise and drive fish further north to cooler waters. However, the transient population of Great Whites almost certainly hasn’t increased by 10x while the species simultaneously becomes increasingly endangered globally.

Herein lies the challenge of measuring almost anything over long timespans: the actual detection methods and distribution of those methods often improve with both technology enhancements and pure measurement scaling. In the case of Great White Sharks, the advent of high-quality smart phone cameras, recreational drone coverage, and increased tagging of sharks over time have all contributed to a natural inflation of positive, verified shark identification. It’s not difficult to imagine that the use of high quality cameras at the beach and on boats increased by 1,000% between 2013-2021, or that the number of tagged sharks increased by nearly an order of magnitude. It also probably became easier to report and consolidate sightings! The natural result of these changes is that we’d likely see ~1,000% more confirmed shark sightings each year even if the actual amount of Great Whites passing by Cape Cod each year stayed exactly the same!

An extreme example of this phenomenon working in reverse is not too difficult to imagine— if we had no more smartphones allowed at Cape Cod beaches next summer, our “detected” shark value would decrease significantly, but I’d bet anything that the number of Great Whites off the shore stayed roughly the same as this year. Statistical means do exist for estimating the actual increase of an underlying signal over time in the face of advancing detection mechanisms. Inherently, though, fun charts simply showing absolute stats like “Great White Shark Detections by Year” mislead by failing to actually compensate for increased perception. Perhaps unsurprisingly, the same is probably true for many types of sports-related detection and reporting— in particular concussions in the NFL!

Perhaps unsurprisingly, the NFL has relatively poor historical data about concussions in the league before 2015 out in the public, the first year they started officially tracking them. Some data aggregated by independent researchers does exist, though it certainly doesn’t span all the way back to the league’s founding in 1920. Nor does it span back to the 1905 “Death Harvest,” which was Teddy Roosevelt’s term for the year when almost 20 people died playing football, mostly from head injuries. It was… a different time back then I guess…

Before jumping into the data from publicly available sources and creating any preconceived notions and biases, it’s worth generating an understanding about the history of concussion detection mechanisms in the NFL. Due to a large number of highly public lawsuits and exposes about the prevalence and danger of brain injuries in football throughout the 80’s and 90’s, the league established a Mild Traumatic Brain Injury (MTBI) Committee in 2002. The noise around concussions grew through the mid-2000’s into the 2010’s with proven links to CTE (a neurodegenerative disease), and in 2015 the NFL finally started officially tracking and publicizing concussions in the league. In the last 30 years, the tools the league can use to spot potential concussions (and all injuries honestly) improved: camera quality improved, replay technology improved, concussion “spotters” were implemented, the actual tests to identify concussions improved, and understanding the impact of concussions also incentivized players to self-report when they had brain injuries.

An increase in both the proliferation of concussion-identifying mechanisms and an improvement in their abilities should sound quite familiar now! All of these factors would lead one to believe that the detection of concussions in NFL games would have increased greatly from the 90’s, when they were much more of a taboo open secret through today. Even though new rules have started to make the game safer for players and helmet and padding technology has improved significantly to help counteract an increase in brain injury rates, it turns out, that’s still true!

Looking at the data in this chart in isolation, it’d be easy to believe that concussion rates in the NFL have ballooned since the late 90’s to today— nearly doubling between 2000 and 2015! However, we now have reason to be skeptical about that conclusion given how far concussion detection mechanisms have advanced and how much more transparency it demands. Even the study linked above that examined 2008-2015 noted that concussions were likely under-reported by teams, and the data going back from 1997-2007 was all gathered from reports by team physicians and trainers. You still hear rumblings today about players trying to avoid concussion protocol, but back then there wasn’t much formal protocol to begin with, so it’s very likely we missed actually identifying and/or reporting many more concussions.

We’ve even recently seen a downtick in identified and reported concussions in the NFL over the last decade, which in some ways might be a reason for optimism about the future! Despite the NFL’s reporting requirements and ID mechanisms both improving, the total rate of concussions has dropped, most likely as a result of improved safety rules and equipment. However, the game continues to get faster every year with bigger impacts, and it’s always possible that we’ll have a technology breakthrough that helps us actually detect even less severe brain injuries that count as concussions. This innovation could lead to another large increase in detected concussions across the NFL, which might seem bad on the surface, but speaks more to the lack of sophistication and detection we may have now— somewhat counterintuitively, it would probably be a good thing for player health and safety!

I mentioned previously that I might extend this article into a series on injury trends in the NFL, and perhaps sports more broadly. There’s so many interesting topics to dive into like the danger of turf fields, increasing Achilles injury rates, and much more. If this type of regular series would be of interest, please don’t hesitate to reach out!

Hopefully this relatively brief article provided an interesting and unique look at the NFL’s expanding (and maybe contracting?) brain injury challenge. Or, in absence of that, a childlike spark to get interested in sharks again!

[MEME]

The idea of consumables as a cost category feels far more apt and ubiquitous in industries such as manufacturing/assembly, construction, and lab-based research where the process of creating a product naturally requires disposal inputs. However, if you consider the revenue-generating output of a pro sports team to be consumer entertainment (which it is), it’s also fascinating to consider what actually qualifies as a consumable, and how the costs compare between sports and might correlate to the overall popularity of a sport at the global scale. Of course, to actually make all these comparisons, we need to have a shared understanding of what actually defines a consumable (at least in my own jargon-light terms)!

Moreso than any other newsletter I’ve published, I should preface that this article contains no financial advice whatsoever. I emphasize that because I’m about to distill some very detailed and nuanced accounting practices that I don’t fully understand into some over-simplified categories and delineations. I believe that everything I’m about to tell you is correct, and at the very least is a useful framework for thinking about the high-level cost structure of a business. However, if you happen to be the newly-appointed CFO of a professional sports team and have no prior experience in finance, please verify your own situation with your CPA(s) and controllers.

At the very highest level, there are two major buckets that business expenses fall into: Capital Expenditure (“CapEx”), and Operating Expenditure (“OpEx”). CapEx costs typically encompass large, upfront purchases that are planned to provide long-term value for a business and generate or increase company revenue in some form. The CapEx bucket contains purchases like manufacturing equipment, purchased real estate, IT infrastructure, and trucks that can often be depreciated or amortized over time. On the flip side, OpEx typically includes smaller, ongoing costs required to keep a business operating like employee payroll, rent, maintenance costs, utilities, etc. As opposed to CapEx, OpEx purchases not anticipated to provide long-term value for a company and are generally expensed immediately.

As mentioned at the start of the article, CapEx investments tend to dominate the public’s mindshare around pro sports team costs, especially because the public often get asked to help foot the bill! Flashy purchases like new stadiums, team planes, and practice facilities all fall under the bucket of CapEx investments. However, you likely can also depreciate some funnier sports infrastructure items in the bucket of CapEx as well, like Zambonis or skeleton sleds or cycling team busses among a nearly endless list of long-term investments by a team. Interestingly, though, moving beyond professional sports teams or leagues whose job includes managing sports-related infrastructure (fields, arenas, golf courses, etc.), individual athletes have very few, if any, real CapEx investments themselves. Maybe something like an amateur’s race car or sail boat could qualify as CapEx, but lower-cost gear like skis, skates, cleats, and balls are all designed to be replaced and/or become obsolete on relatively short timeframes.

As the focus of this article, consumables fall under the huge remaining bucket of OpEx that covers the vast majority of sports gear and equipment. OpEx can be broken down into a variety of categories, but three stand out as the major groups. Buckets of “fixed,” “semi-variable,” and “variable” costs are used fairly ubiquitously in both the manufacturing space and business more broadly, and thankfully each of these classifications is somewhat self-explanatory. “Fixed” costs are fixed and do not change in time based on the overall productivity or outputs of a business, like office rent or insurance premiums. “Semi-variable” costs fluctuate around fixed amounts based on business output like sales salaries (w/commission) or electric bills. Finally, “variable” costs completely depend on the overall productivity of a business— if no work occurs, variable costs can disappear! Items like raw material inputs to a manufacturing process, maintenance costs, and hourly labor count as purely variable costs.

In the world of pro sports, I think the easiest way to illustrate these three categories is to think about a team that for whatever reason is incapable of playing games but still must pay its players and staff (maybe like during early COVID, actually…). With no ticket and TV revenue generated from playing games, a team still must shoulder some fixed costs including arena property taxes and insurance on top of some baseline salary for players and staff— teams incur these costs at the same level with a full season or no season! Teams also have semi-variable line items that fluctuate with the number of games played, but have some static cost element as well, like the stadium electric bill or the incentive portion of players’ pay. Finally, with no games in a season, a teams’ variable costs like fuel for planes, hourly concession workers, and even replacement gear for players drop to $0 theoretically.

The tree diagram above gives it away, but consumables tend to live in the final bucket of purely variable costs in most cases. Despite possibly coming across as a low-cost line item on a team’s overall budget, hopefully I’ve planted the inkling that all of a team’s consumables can actually add up to be quite expensive in many sports. As it turns out, when nearly every piece of equipment and gear for an athlete is effectively expected to have a useful shelf life of a few games (or less), the small expenses start to add up quickly.

Professional sports as a whole present a few somewhat comical examples of high consumables costs. Formula 1 teams spend about $1.7 million per team per season on tires, while each NHL team shells out at least $500,000 a year on sticks alone, snapped and discarded with depressing regularity. Some consumables like the annual ~$70k of basketball shoes or the $250k+ of bikes for a pro cycling team are often covered by a sponsor or the brand manufacturer, but only at the highest levels of a given sport. Interestingly, every league has different rules about who covers which ongoing costs associated with player gear/equipment, but for many professional sports like the NBA, NFL or soccer, where top player salaries are astronomically high and gear/equipment costs are relatively low, a team’s total player salary (semi-variable if you include incentives) still dwarfs the consumables costs.

With that said, it’s still interesting to see a list of the most expensive single consumable in many popular sports around the world for a sense of scale!

NOTE: The figures in the table above are estimated. Exact data for global participation estimates and total units/year for a professional sports team are rarely available and these are constructed with best available info.

Apart from my complete guess at what the most expensive consumable is for a horse, this table also highlights some very interesting unique high-cost items like cricket bats and field hockey sticks, both of which I thought were maybe 1/3 their actual cost! An interesting follow-on thought experiment is to think through the number of individual consumables each sport listed has, since I’ve just captured the most expensive individual item. For example, when you combine the 8+ sizable equipment costs for a sport like ice hockey where the NHL where teams cover all gear for players, the annual costs can often rival the cost of star player salaries. The relative cost of consumables for a lower-tier professional sports team or even a college team becomes an even bigger line item compared to on-field talent once player salaries drop down from millions of dollars per year. For amateur and youth sports teams, consumables costs in the form of gear and equipment comprise nearly the entirety of a team’s “losses” for a given year.

I have a hypothesis that in general a sports’ most expensive consumable likely correlates to its overall cost for a youth or amateur athlete paying for everything (which might be misinformed but I can’t think of any clear counterfactuals). In addition, I’d suspect that that the global popularity of a sport is inversely correlated to its overall cost, all else equal. Plotting the global popularity of a sport against its most expensive single consumable for the team sports listed above should in theory demonstrate this linked correlation… right?

Well, maybe not so much. There’s an extremely low correlation between a team sports’ most expensive consumable (piece of equipment) and its participation at the global level. This is almost certainly partially due to a low sample size of data, but I think speaks to the facts that a) equipment alone doesn’t dictate total sport cost, and b) sport popularity is dependent on so many factors outside of average cost for a regular player. The world of sports has so many interesting regional fascinations, geographic constraints, and popularity trends that on the ladder of features impacting global popularity, cost may be relatively low except in the most extreme cases. Separately, I would love to see how the number of global participants in a sport correlates to its viewing popularity… which might be a topic for a future article.

Beyond the impact on different levels of professional team and individual athletes, it’s also just pure fun to see how big seemingly random consumables markets in sports actually are at a global scale. For instance, the global tennis ball market is estimated to total ~$1.5B in annual value, which must make it the biggest individual market for a laundry tool. Conversely, cricket (which has 2.5 billion fans globally) only yields a ~$250M ball market. Shockingly, shuttlecocks for badminton have a global market of nearly $700M— nearly three times as large! Even lacrosse sticks have a total market size ~$500M. The comparisons could go on and on, and I highly recommend just Googling “[insert sport] equipment market value.” It might be the most fun and surprise I’ve had searching the web in years.

The fact that so many seemingly niche sports gear and equipment products sustain such large global markets speaks to the constant turnover of these products as they get used and disposed by users. While this broad bucket of “consumables” might not yield the biggest cost impact for major pro sports teams, its place within the cost structure of amateur and youth athletics is hard to ignore. So at the end of the day, though, is my fascination about an annual cost of $1M in pointe shoes for the NYC Ballet actually just a drop in the bucket of the global badminton birdie industry? Perhaps, but I’m glad it sparked me to think through the manufacturing cost architecture in the world of sports. I’m also glad I did enough research to make this comparison for the first time in history.