Tag: Sports Technology

Kona 2026: One Day, One Island – Data, Fuel and Heat at the IRONMAN World Championship

For the first time since 2019, the professional men and women race the IRONMAN World Championship on the same day in Kailua-Kona. It is also the final stop of the 2026 IRONMAN Pro Series, with 6,000 points on offer to each winner, so the race carries both a world title and a season title. The men start at 6:20 a.m. local time, and by the time the leaders reach the Energy Lab the conversation will, as always in Kona, be about heat, fuelling and who managed both best.

In this post I bring together three things: what the professional race data tell us, what the newest research says about carbohydrate intake during racing, and what we know about heat acclimatisation and the conditions expected on the Big Island.

The data: trends, conditions and predictions

To support this preview I built an interactive Long Distance Triathlons Dashboard, which brings together professional-only results from IRONMAN, IRONMAN 70.3 and T100 racing (sourced from PTO Stats) with race-day environmental data from Open-Meteo. A few design choices are worth explaining, because they matter for how the numbers should be read.

First, split-time trends use the average of each race’s top three finishers rather than the winner alone, with a ±1 SD band. A single winning time is a noisy signal; the top-three average, and the width of that band, tells you both how fast the front of the race was and how close the podium fight was. Second, conditions are matched to performance using WBGT (wet bulb globe temperature) rather than air temperature for the bike and run, since WBGT captures humidity and radiant load, which is exactly what makes a 30 °C day in Kona so different from a 30 °C day somewhere dry. Wind is averaged across the 07:00–16:00 window that covers professional race time, rather than taking a single daily gust. Third, the correlation plots use every individual finisher’s split against the conditions of that race, not race averages, so you can filter by brand and see how the relationship holds up.

The Rankings & Predictions page applies a simple form-based model to the start lists. It is deliberately a heuristic, not a crystal ball: it rewards consistency and recent results, and it cannot know who has been ill, who crashed in training, or who has nailed their heat preparation. It is, however, a useful benchmark against which to judge the race once it is run.

The historical benchmarks are formidable. Patrick Lange’s course record of 7:35:53 in 2024 was built on a 47:09 swim, 4:06:22 bike and 2:37:34 run, and the same race saw Sam Laidlow ride 3:57:22. Lange, now 40, has said this will be his last IRONMAN. In the women’s race, Solveig Løvseth won on her Kona debut in 2025 in 8:28:27, just 35 seconds ahead of Kat Matthews, who set a women’s run record of 2:47:23, with Laura Philipp third in 8:37:28. That race is remembered as much for Taylor Knibb, who was leading with around two miles to go before collapsing – a reminder that in Kona the margin between a world title and a medical tent is thin.

On the men’s side the defending champion Casper Stornes is out after a training crash, which opens the race up. Laidlow arrives unbeaten in four races in 2026, including Challenge Roth and an IRONMAN Lanzarote win by almost nine minutes. Kristian Blummenfelt leads the Pro Series standings, and Gustav Iden, Marten Van Riel (on his Kona debut), Magnus Ditlev and Lange complete a field with four former world champions. Independent modelling by Thorsten Radde at Trirating suggests the leading men share roughly three quarters of the win probability, against about 85% for the leading women – in other words, a surprise is more likely in the men’s race. That is consistent with what I see in the data: the women’s front-runners have more head-to-head history on this course and in these conditions.

Fuelling: how much carbohydrate is enough?

Few areas of endurance practice have moved as fast as in-race fuelling. The 2016 ACSM sports nutrition guidance recommends up to 90 g/h of carbohydrate from multiple transportable carbohydrates (glucose–fructose mixtures) for events longer than 2.5–3 hours. In the field, however, many professionals now report targets of 120 g/h and, in some cases, far higher. Two papers published this year help separate what is supported by evidence from what is enthusiasm.

Morton and colleagues’ review in the Journal of Nutrition (Morton et al., 2026) revisits more than a century of work on carbohydrate and endurance. Their conclusion is that the upper limit of recommended intake could reasonably move from 90 to 120 g/h for trained athletes, since both exogenous and whole-body carbohydrate oxidation can increase at these intakes, with possible benefits for durability and economy. They are explicit, though, that doses of 120–200 g/h seen in the field are not yet substantiated by research, and they propose a more individualised model that accounts for carbohydrate blend and ratio, format, gut training and environmental conditions.

A new study in Medicine & Science in Sports & Exercise (Martyn et al., 2026) tested this directly. Eight trained men cycled for three hours followed by a capacity test, ingesting 10, 90, 120 or 150 g/h (maltodextrin–fructose at 1:0.8, with stable-isotope tracers). Exogenous oxidation rose with dose (1.23, 1.48 and 1.62 g/min at 90, 120 and 150 g/h), but whole-body carbohydrate oxidation was identical at 120 and 150 g/h (2.6 g/min). Exercise capacity was clearly impaired at 10 g/h but did not differ among the higher intakes, and the modelled optimal dose was about 107 g/h (95% CI 92–122 g/h). Just as important, the between-athlete spread in exogenous oxidation was large and grew with dose.

For a Kona athlete the message is practical. Somewhere around 90–120 g/h is where the evidence currently sits; going beyond that is unlikely to add whole-body carbohydrate use and increases the volume of fluid and sugar the gut has to handle. In the heat this matters even more, because splanchnic blood flow is reduced and gastrointestinal distress becomes a major cause of underperformance. Recent work from Monash (Hillemacher et al., 2026) shows that symptom reports alone probably underestimate gut disturbance during exertional heat stress, and a survey at the 2025 Western States 100 (Mougin et al., 2026) found that every finisher under 18 hours had a structured carbohydrate plan, compared with 69% of the slowest finishers, with gastrointestinal symptoms reported by 63% of finishers. The athletes who fuel best on Saturday will be those who rehearsed their exact race-day intake, in the heat, many times beforehand.

Heat: acclimatisation and the Kona forecast

October in Kona typically means highs of 30–32 °C and humidity around 80%, with the lava fields and asphalt of the Queen K adding considerable radiant heat. The ocean is around 25–27 °C, so the swim is non-wetsuit. The National Weather Service forecast issued on Friday afternoon for Saturday calls for sunny conditions with isolated showers in the morning, becoming partly sunny with scattered showers in the afternoon, highs of 84–89 °F (29–32 °C) near the shore, and light winds becoming westerly up to 10 mph in the afternoon, with a 40% chance of rain. Light winds are good news for bike times but bad news for heat dissipation: less convective cooling on the bike and in the Energy Lab means a higher effective heat load, and afternoon cloud and showers could become the most important variable for those still running. On the dashboard you can see how WBGT and wind have related to bike and run splits across previous editions.

The physiology of heat acclimation is well established, but recent studies have refined the practical questions of dose, method and side benefits. A randomised trial in endurance-trained athletes (Snape et al., 2026) showed that just five days of isothermic acclimation in hot, humid conditions (32 °C, 70% RH) lowered resting heart rate by 8 bpm, expanded plasma volume by about 7% and increased sweat loss by around 500 mL during a heat stress test – the classic adaptations that protect cardiac output and thermoregulation on a long day. Renal stress markers were not altered, which is a useful reminder that acclimation improves tolerance but does not remove the physiological cost of exercise in the heat.

Method matters. In well-trained cyclists, six days of post-exercise hot water immersion reduced perceptual strain but did not improve time-trial performance in warm or hot conditions (Kjertakov et al., 2025), suggesting passive approaches are not a substitute for active exercise-heat exposure in already fit athletes. Interestingly, training in the cold while wearing layered clothing reduced perceived exertion and thermal sensation in subsequent heat exposure (Yamaguchi et al., 2026), although without measurable changes in sweat rate or core temperature.

Heat training is also increasingly used for its haematological effects, something the Norwegian group in this field has made very visible. In cross-country skiers and biathletes, adding three weekly heat-suit sessions to a moderate-altitude camp increased haemoglobin mass by 2.9%, compared with 1.2% with altitude alone (Rønnestad et al., 2026), and in elite cyclists five weeks of heat training produced haemoglobin mass gains comparable to a live-high train-high camp (Cubel et al., 2025). In neither study did this translate into clear performance gains, which is an honest caveat: the adaptations are real, but how much they matter on race day is still debated.

The key point, from both the literature and my own experience working in Doha, is that heat acclimatisation is not a single intervention but a process that interacts with everything else: hydration, sodium, cooling strategies, pacing and, crucially, fuelling. Heat increases carbohydrate dependence while simultaneously reducing the gut’s ability to deliver it. The athletes who arrived in Kona early, trained in the conditions and rehearsed their nutrition in that environment will have a real advantage, whatever their power numbers say.

What to watch

If the winds stay light, expect fast bike splits and a large group reaching T2 together, which shifts the race to the marathon and to whoever has managed heat and fuelling best through the first six hours. If the afternoon clouds and showers arrive, they may favour the chasers. And if history is any guide, the athlete leading with 10 km to go usually wins – unless, as Knibb discovered last year, the heat decides otherwise.

Explore the full data, filter by brand and race, and check the predictions at ironmandt100analysis.netlify.app. I will revisit the predictions once the results are in.

References

Cubel C, et al. Haematological adaptations to high-altitude and heat acclimation training in elite male cyclists. Exp Physiol. 2025. doi:10.1113/EP092968
Hillemacher K, et al. The relationship between biomarkers of exercise-induced gastrointestinal syndrome and exercise-associated gastrointestinal symptoms. Sports. 2026. doi:10.3390/sports14060248
Kjertakov M, et al. Limited benefit of repeated post-exercise hot water immersion on exercise performance of well-trained cyclists in warm and hot conditions. Eur J Appl Physiol. 2025. doi:10.1007/s00421-025-06041-4
Martyn HJ, et al. Effects of 10, 90, 120 and 150 g/h CHO on substrate metabolism, exogenous oxidation and exercise capacity: implications for CHO personalization. Med Sci Sports Exerc. 2026. doi:10.1249/MSS.0000000000004176
Morton JP, et al. From metabolism to medals: contemporary perspectives and revisiting carbohydrate guidelines for fueling endurance athletes during exercise. J Nutr. 2026;156(5):101442. doi:10.1016/j.tjnut.2026.101442
Mougin L, et al. Heat preparation and nutrition strategies for a 100-mile ultramarathon in extreme heat: a questionnaire study at the Western States Endurance Run. J Sci Med Sport. 2026. doi:10.1016/j.jsams.2026.05.014
Rønnestad BR, et al. Heat training augments hemoglobin mass during a moderate-altitude training camp in well-trained endurance athletes. J Appl Physiol. 2026;140(5):1298-1310. doi:10.1152/japplphysiol.00777.2025
Snape D, et al. Five days of heat acclimation improves cardiovascular and thermoregulatory responses without altering renal stress biomarkers in endurance athletes. PLoS One. 2026;21(8):e0345346. doi:10.1371/journal.pone.0345346
Yamaguchi K, et al. Short-term endurance training in the cold with layered clothing mitigates perceptual strain during exercise in the heat. Eur J Appl Physiol. 2026;126(10):5543-5552. doi:10.1007/s00421-026-06319-1

Race and forecast information from USA Triathlon, Triathlete, 220 Triathlon, Big Island Now and the US National Weather Service. Literature retrieved via PubMed.

We can all build our own tools now: what Claude and GPT-6 Astra mean for researchers (and some apps worth a look)

Back in March 2011 I wrote a short post about Google Labs’ anatomy tool. At the time, being able to rotate a 3D human body in a web browser felt like science fiction, and it had taken one of the biggest technology companies in the world to build it. Fifteen years later, a single developer released an open-source anatomy explorer that takes the human body apart into 2,234 individually selectable pieces — two days after the AI model she used to build it became available.

That, in one sentence, is what this post is about.

Over the summer I have written here about the tools I have been building with AI: a lactate threshold analyzer, a ten-year triathlon heat-strain study run end-to-end with AI, a systematic review on lactate supplements and a rebuilt football injury dashboard. None of them needed a development team. What has changed in the last couple of weeks is that it is no longer just me, and no longer just one company’s tools. Researchers, clinicians and educators everywhere are shipping genuinely useful applications, and some of them are very good indeed. So I wanted to pull together what has happened with the models, show you a few of the apps that caught my attention, and be honest about where I think we need to be careful.

How it used to work

For most of my career, getting a bespoke tool built followed the same script. You wrote a specification. You found a developer, or a budget, or both. You waited. You tested what came back and discovered the developer had misunderstood the science. You waited again. Many good ideas never made it past the specification stage, and many of the tools that did were abandoned the moment the grant ran out or the developer moved on.

That script has been torn up. The distance between “I wish I had a tool that did this” and a working link you can send to colleagues is now measured in days, sometimes hours.

What changed this summer

On the Anthropic side, the Claude 5 generation arrived one tier at a time rather than in a single big launch: Claude Opus 4.8 at the end of May, Claude Fable 5 and Mythos 5 on 9 June, Sonnet 5 on 30 June, Opus 5 on 24 July, and Fable 5.1 and Mythos 5.1 on 1 September. Opus 5 is what I used for the lactate review, and the combination of its price and a one-million-token context window is what makes it practical to keep a whole codebase, a dataset and a protocol in view in a single working session.

Fable 5.1 and Mythos 5.1 are the same underlying model with different safeguards: Fable is available to everyone, while Mythos is restricted to vetted cybersecurity and life-sciences organisations. The number that caught my eye was on Terminal-Bench-Science, a benchmark of agentic scientific research tasks, where Fable 5.1 scored 52.6% against 24.7% for Fable 5 only three months earlier. These are vendor-reported benchmarks, so treat them as such, but a doubling in a quarter is not nothing. Cache reads also became 75% cheaper, which sounds boring but matters a lot when an agent spends a long build session re-reading the same files.

The models are only half the story, though. Claude Code, Cowork, Claude Design and artifacts that can store data mean the output of a conversation is no longer a chunk of code you have to assemble yourself — it is a working tool you can open, test, share and keep improving.

On the OpenAI side, GPT-6 Astra was released on 3 September, first to a limited set of organisations and then to ChatGPT Plus, Pro, Business and Enterprise users and the API over the following days. OpenAI pitches it as state of the art for computer use, coding and scientific work. It is also the first OpenAI model to reach the “Critical” cybersecurity threshold in its own Preparedness Framework, which is why the rollout was staged — more on that later. The other piece worth knowing about is ChatGPT Sites, in public beta since July, which builds a web app from a conversation and publishes it straight to a live chatgpt.site address with the hosting included. At launch it was not available in the EEA, the UK or Switzerland, so not everyone reading this will have it yet.

Summer 2026: frontier model releases and the tools built with themAnthropic (orange) and OpenAI (teal) releases, May–September 2026, against examples of tools shipped in the same windowFable 5 / Mythos 5access paused(US export controls)Frontier model releasesTools shipped (examples)Claude Opus 4.8Claude Fable 5& Mythos 5Claude Sonnet 5Claude Opus 5Claude Fable 5.1& Mythos 5.1GPT-6 AstraLactate ThresholdAnalyzerTen-year triathlonheat-strain studyLactate supplementsystematic review(Opus 5)Football injurydashboard rebuildTesla Model Xexploded view(Astra)Human Atlas2,234 meshes(Astra)Model release → public 3D anatomy app: 2 daysJun 2026Jul 2026Aug 2026Sep 2026

The summer in one picture: model releases on top, tools shipped underneath. The gap between GPT-6 Astra becoming available and a public, open-source 3D anatomy explorer built with it was two days.

The cost of trying an idea has collapsed. The cost of being wrong has not.

Four tools worth a look

Human Atlas: the body in 2,234 pieces

human-atlas-seven.vercel.app · code on GitHub

This is the one I opened this post with. Developer Ashe Magalhaes had just used Astra to build an “exploded view” of a Tesla Model X in 334 pieces, and then, after joking online about whether it would be weird to do the same with a body, did exactly that. Human Atlas takes the BodyParts3D adult male reference apart into 2,234 selectable meshes across 15 anatomical systems, with 3,432 searchable named structures. You can rotate the body, isolate a single structure, switch systems on and off, and spread the whole thing out into an inventory of every visible piece. It is built with React and Three.js, the code is MIT-licensed, and the anatomy data keeps its own CC BY 4.0 licence.

What impressed me most, though, was not the rendering. It was the README. It tells you what was validated (mesh data, search, layouts on desktop and phone screens, tap-versus-drag handling), what was not tested (performance on physical devices and real multitouch hardware), and that it is an educational explorer, not a diagnostic or surgical tool. That is exactly the standard I would like to see from anyone publishing an AI-built tool.

Human Atlas: 2,234 BodyParts3D meshes in an exploded view, running in the browser with nothing to install.

Immediately after, another developer deployed the female model.

Someone else today deployed the skeleton

Portal Tau: clinical research methods made interactive

investigaciontau.com · Portal Tau

Tau: Investigación Clínica y Epidemiológica is Dr Joshuan J. Barboza’s Spanish-language platform for training researchers in systematic reviews, research methods and scientific publishing. Its companion Portal Tau shows what one researcher-educator can now put online. There is a Journal Match tool that scores how well your manuscript fits across 32,193 journals using Scimago, DOAJ and OpenAPC data; an explorer comparing the publication fees journals advertise with what institutions actually paid; a ten-module evidence-based medicine calculator; and a clinical trials simulator.

The simulator is the piece I would use in teaching. It runs phase I dose-escalation designs participant by participant — BOIN, Keyboard, CRM, the classic 3+3 and accelerated titration — as well as phase II designs (Simon, Gehan, Bayesian and randomised), and then simulates thousands of trials with Monte Carlo methods. Anyone who has tried to explain to a room of students why the 3+3 design is inefficient will know that running a thousand simulated trials in front of them does more than any slide ever will. And the whole suite sits on free static hosting: no server bill, no IT department.

The clinical trials simulator in Portal Tau: designs that usually live in textbooks, made interactive.

Shoulder Atlas: the examination in motion

shoulder-mechanics-atlas.geraldmd.chatgpt.site

If I had to put one of these in front of physiotherapy and sports medicine students tomorrow, it would be this one. Shoulder Atlas animates 12 clinical examination tests on a 3D right shoulder: empty can (Jobe’s), full can, Neer’s, Hawkins–Kennedy, painful arc, resisted external rotation, lift-off (Gerber’s), belly-press, Speed’s, Yergason’s, cross-body adduction and passive external rotation.

Each test plays out in stages. For the empty can, you watch the arm elevate in the scapular plane, the thumb turn down, and the examiner apply resistance, with live readouts of arm elevation, humeral rotation and elbow flexion. Separate overlays show passive guidance, examiner resistance and patient effort, and the target structures light up — supraspinatus and the subacromial region, in this case. Alongside the animation you get how to perform the test, what it loads, what counts as a positive finding and how to interpret it, with links to Stanford Medicine 25, the AAFP and ShoulderDoc.

Two things make it a particularly good example for this post. The first is where it lives: a chatgpt.site address, which is where ChatGPT Sites hosts what it builds, so it went from conversation to public link without anyone configuring a server. The second is its “About the model” panel, which is a small masterclass in honest scoping. Bones and muscle surfaces come from BodyParts3D; the bursa, capsule and tendon paths are simplified overlays; scapular movement is illustrative; there is no collision, force, tissue-strain or pain solver; and the angles describe the animation, not measured patient motion. It calls itself a teaching prototype, not a validated diagnostic simulator, reminds you that no single test confirms a diagnosis, and says plainly that instability and labral tests are not in this first version. I have seen commercial products that are a lot less candid.

Shoulder Atlas: Tests can be animated.

What these tools have in common

The hard part is no longer the code. In everything I built this summer, getting something to render was never the problem. The work was deciding which method to use, what to label as an estimate rather than a measurement, and what the tool should refuse to tell you. The science lives in the specification. Someone who understands why a modified Dmax threshold differs from a fixed 4 mmol·L⁻¹ one will write a far better specification than a developer who does not — and now that person can build the tool themselves.

Open data is doing a lot of quiet heavy lifting. Human Atlas and Shoulder Atlas both exist because BodyParts3D is openly licensed. Portal Tau works because Scimago, DOAJ and OpenAPC publish their data. The models get the headlines, but without open datasets they would have very little that is useful to build on.

The expert can build exactly what their practitioners need. A commercial product is designed for the average user. A tool built by someone in the field can encode the exact protocol, the exact caveats and the exact outputs a particular group needs — and be fixed the same afternoon someone spots a problem.

Being honest about limits is becoming the marker of quality. When building is cheap, the tools worth trusting are the ones that tell you what was checked, what was not, and what they are not for.

Where I would be careful

I am enthusiastic about all of this, but it would be a strange post from me if it did not come with caveats.

  • Plausible is not the same as correct. In the lactate review, the AI produced a chemistry figure showing the wrong mirror-image form of the molecule, and it looked perfectly fine. Apps fail in exactly the same way: a chart that renders is not a chart that is right. Every calculation in a tool should be checked against values worked out independently, by hand if necessary.
  • Educational is not clinical. The moment a tool is used to inform the diagnosis or treatment of a specific patient or athlete, it is in regulated territory. The best examples above say explicitly that they are for education. That line needs to stay bright.
  • Data protection does not get faster just because development does. Identifiable athlete or patient data should not go into a quickly deployed web app, or into a prompt, without the same governance you would apply anywhere else.
  • Security matters more, not less. The same capabilities that let a model build an app let it find the weaknesses in one. OpenAI staging Astra’s release because of its cyber capabilities, and the safeguards Anthropic layers on its Mythos-class models, should remind us that something deployed in an afternoon has probably not been hardened.
  • Tools that are easy to build are easy to abandon. Dependencies change underneath you — my own football dashboard showed a blank page for some people until I stopped it relying on an external charting library. Access to the models can change too: in June, Anthropic suspended access to Fable 5 and Mythos 5 for around three weeks to comply with US export controls before restoring it. Keep your code, your data and your exports somewhere you control.

In conclusion

A year ago, most of the tools in this post would have needed a team, a budget and several months. Now they need a researcher with a clear idea, a good specification and the discipline to test what comes back. And this is true whether you work with Claude or with ChatGPT — the capability is no longer tied to one company.

I do not think the people who benefit most will be those who build the most apps. It will be those who specify carefully, validate against known answers, write down what they have not tested, share their code, and keep themselves firmly in the loop. So my encouragement to colleagues is simple: build the tool you have always wished existed, put it online, be honest about its limits, and invite people to break it.

And if you have built something along these lines in sports science or sports medicine, send it my way. I would love to feature a few more.


Links mentioned in this post

Model release dates and benchmark figures come from Anthropic and OpenAI announcements and contemporaneous coverage; benchmarks are vendor-reported. Tool descriptions are based on the public sites and repositories as of 11 September 2026. I have no affiliation with any of the third-party tools mentioned. This post was drafted with the help of Claude and edited by me.

From Splits to Heat Strain: A Ten-Year Triathlon Study Built End-to-End with AI

Regular readers will know I have spent the last couple of months playing with AI agents to scrape and visualise publicly available sport data — first football muscle injuries, then a live dashboard for the Giro d’Italia. Those were fun, low-stakes experiments to learn what the tools could do. This post is the point where the experiment turned into something more serious: a full research study, now posted as a preprint, built almost entirely with the same family of AI tools.

I planned, conducted and executed this study — and developed the accompanying digital twin — using Claude Sonnet 5 and Claude Opus, together with Claude Cowork, Claude Code and Claude Design. Cowork orchestrated the data gathering and organisation across ten years of race results and weather records, Code built the statistical models and the digital twin engine, and Design shaped how the outputs are presented. I stayed firmly in the loop throughout as the domain expert: setting the research questions, checking the physiology and the methodology at every step, validating the data, and deciding what the numbers actually meant. I also modified some of the coding as things progressed to develop various analytics steps.

Why triathlon, why heat

Triathlon is an Olympic sport, and elite Olympic-distance racing is shaped by the interplay of swim-bike-run pacing, transition efficiency, the quality of the field, and — increasingly — the heat athletes race in. Major championships are more and more often held in hot conditions, which matters enormously for how athletes and support staff plan training, pacing, cooling and acclimatization. Despite there being a decade of publicly available race results and weather records out there, nobody had linked the two together at scale. That gap was the starting point.

The study had four aims: characterise the performance signature of a podium finish; reconstruct the thermal environment of championship venues over ten years; identify which athletes seem resilient to heat; and, building on all of that, prototype a digital twin that could support race planning.

Ten years of racing, in numbers

A few things stood out. Bike and run splits each contribute roughly equal unique variance to total race time, but the run leg is the real discriminator between the podium and the rest — it was the single most important feature in the podium-prediction models, at 45.5% importance for men and 48.9% for women. Somewhat counter-intuitively, the slope linking heat to performance did not reach statistical significance for either sex (men p=0.065, women p=0.104), which is a useful reminder not to over-interpret heat effects from headline temperature alone. DNF rates, on the other hand, told a clearer story, ranging from 12.7–16.3% for men and 11.7–18.8% for women across World Triathlon’s Green and Blue flag heat-risk categories.

Building the digital twin

The last aim — and the part I am most excited about — was turning ten years of descriptive analysis into something forward-looking. The digital twin prototype couples a performance model with a thermo-physiological heat-strain model, so it can be used to explore pacing, cooling and acclimatization decisions ahead of a race rather than just explaining results after the fact. On a temporal hold-out (training on earlier years, testing on later ones — the fairest test for something meant to inform future decisions), it achieved a mean absolute error of 0.490 z-units for men and 0.468 for women (r=0.383 and 0.295 respectively). Those are honest, prototype-grade numbers, not a finished predictive tool, and I say so explicitly in the paper.

The usual health warning

This is a preprint. It has not been peer reviewed, and I want to be upfront about that rather than let the AI-workflow angle overshadow it. The underlying data are scraped from publicly available sources, so they carry all the usual caveats about completeness and accuracy that come with that. What I can say is that the statistical approach, the modelling choices and the interpretation were all reviewed and directed by me at every stage — the AI tools accelerated the mechanics of gathering, structuring, analysing and building, but the scientific judgement was, and had to be, human. Critical thinking stays firmly the job of the person in the loop, whatever is doing the typing.

Why this matters to me

I hope this prototype is the beginning of something bigger rather than a one-off curiosity. There is an enormous amount of publicly available data across sport that nobody has the time to properly interrogate and organise — results archives, weather records, GPS feeds, injury registries. What changed for me this year is that a single person, using these AI tools well, can now plan, run and analyse a study of this scale in weeks rather than requiring a full research team and many months. I want to keep using that capability to ask more questions like this one across sports science and sports medicine, and to be transparent about the process as I develop more tools and research questions.

If you want to dig into the full methods, results and figures, the preprint is here: From Splits to Heat Strain: A Ten-Year Analysis of Performance Determinants and a Digital Twin Prototype for Olympic-Distance Triathlon Developed Using a Generative AI Workflow (DOI: 10.21203/rs.3.rs-10216961/v1). As always, comments and critique are very welcome — that is rather the point of putting it out as a preprint while it goes through formal peer review.