Tag: Data

Data, data analysis, Data Analytics, Sports Science, Triathlon

A New Dashboard for Long-Distance Triathlon: Tracking the 2026 Ironman, 70.3 and T100 Season

Long-distance triathlon has never generated more data. Between the Ironman Pro Series, the Ironman 70.3 circuit and the PTO’s T100 Triathlon World Tour, the professional field now races across dozens of events a year, each producing split times, finishing margins and — if you go looking for it — a rich picture of the environmental conditions athletes faced on the day. The problem is that this information sits scattered across result pages, weather databases and is rarely analysed as a whole. So I built something to fix that.

I’ve developed an interactive Long-Distance Triathlons Dashboard, together with an open data repository, to bring professional results from Ironman (full distance), Ironman 70.3 and T100 into one place and let anyone explore how performances — and the conditions behind them — are trending. This post walks through what the tool does and offers a snapshot of how the 2026 elite season has played out so far.

The dashboard

The dashboard is live and free to use here: ironmandt100analysis.netlify.app. It uses professional-only results — no age-group data — sourced from PTO Stats (the Professional Triathletes Organisation), combined with race-day environmental data from Open-Meteo. It’s organised into three views.

  • Trends & Conditions — split-time trends across seasons and how race-day conditions line up with performance.
  • Athletes — individual athlete profiles and split histories.
  • Rankings & Predictions — current standings and model-based projections for future races this season.

The core of the analysis lives in the Trends & Conditions view. Rather than tracking a single winner’s time — which is noisy, since one exceptional or off day skews the picture — the trend charts plot the average of each race’s top three finishers for every split (swim, bike, run and overall), with a shaded band showing ±1 standard deviation across that podium. A tight band means the top three were closely matched; a wide band means the race blew apart. You can filter by race brand to compare like-for-like distances, and by category to separate the men’s and women’s fields.

Where I think it gets genuinely useful — and where it connects to my longer-standing interest in environmental physiology — is the conditions layer. For every race the dashboard pulls temperature, humidity, wind and the WBGT (Wet Bulb Globe Temperature) heat-stress index, and sets them against performance. There’s a race-level view (winning splits against the day’s conditions) and a finisher-level view, where every individual result is plotted against the condition recorded for that race and a Pearson correlation is computed on whatever subset you’ve filtered to. Water temperature is matched to swim splits; WBGT is used for the bike and run, because those are where combined heat stress bites hardest. A sortable race-by-race table underneath ties it all together — date, event, winner, every split, and the conditions on the day.

A couple of honest caveats are built into the tool. Splits aren’t comparable across brands, because the distances differ (a full Ironman, a 70.3 and a 100 km T100 are three different animals), so the “fastest recorded splits” are grouped by brand. And the wind figure is each day’s maximum hourly reading, which can overstate what athletes actually felt during an early-morning start. Transparency about these limitations matters more to me than a tidier-looking chart.

The open data repository

The dashboard is only half of it. The full data and the code behind it are openly available on GitHub: github.com/drmarcocardinale-hub/IronmanT100-repository. My aim is reproducibility — anyone can inspect how the results were collated, how the conditions were matched to each race, and how the trends and correlations were computed, and can build on it or challenge it. Open data and open code are the right default for this kind of descriptive sports-performance work, and I’d rather the analysis be scrutinised than taken on trust.

The 2026 season so far

So what does the season look like through the middle of 2026? Below are some of the headline professional results across the three series. These are selected highlights — the full race-by-race picture, with every split, lives in the dashboard.

T100 Triathlon World Tour

The big structural change for 2026 is that the T100 now runs separate men’s and women’s events through the regular season — four standalone races for each field — before both converge at the Qatar World Championship Final in December. On the men’s side, reigning champion Hayden Wilde opened in devastating form, taking Singapore by more than six minutes (3:21:58). Rico Bogen then successfully defended his San Francisco title over Lasse Nygaard Priester, with Wilde third. In the women’s races, Taylor Knibb edged a tight season opener on the Gold Coast, and Georgia Taylor-Brown ran down Julie Derron in Spain to claim her first career T100 title.

T100 race (2026)FieldWinner
Gold CoastWomenTaylor Knibb
SingaporeMenHayden Wilde
Spain (Pamplona)WomenGeorgia Taylor-Brown
San FranciscoMenRico Bogen

Ironman (full distance)

The full-distance season delivered one of the standout performances of the year: at Ironman Texas (the North American Championship, 18 April), Kristian Blummenfelt stopped the clock at a barely believable 7:21:24 — reported as the fastest full-distance time on record — outrunning Marten Van Riel late on. The women’s race the same day went to Solveig Løvseth in 8:11:09, ahead of Taylor Knibb (8:14:48) and Marta Sánchez (8:31:06). Løvseth then completed a full-distance double at the Ironman European Championship in Hamburg, holding off Laura Philipp to win in 8:11:11, just over a minute clear — a remarkable run of form from the reigning Ironman world champion. And earlier in the year at Ironman New Zealand, Trevor Foley and Kat Matthews took the men’s and women’s titles.

Ironman 70.3

The 70.3 circuit has been relentless. Marten Van Riel has been close to untouchable over the middle distance, and Kat Matthews has strung together a run of results that mark her as one of the athletes of the season so far. Kristian Blummenfelt showed his range with a narrow win in a Geelong thriller. A snapshot of the middle-distance winners:

Ironman 70.3 race (2026)MenWomen
GeelongKristian BlummenfeltKat Matthews
Aix-en-ProvenceMichele BortolamediMarjolaine Pierré
ElsinoreMarten Van RielKat Matthews
SwanseaHarry PalmerLizzie Rayner

Why look at it this way

Naming winners is the easy part. What interests me is the layer underneath: how tightly bunched the podiums are becoming, where the time is genuinely being won and lost across the three splits, and how much of the day-to-day variation in performance tracks with heat, humidity and wind rather than fitness alone. A 7:21 at Texas and an 8:11 at a hot, humid European Championship are not the same test, and treating conditions as a first-class variable — not an afterthought — is exactly what the dashboard is designed to make possible.

This is very much a living project. I’ll keep adding races as the 2026 season continues toward the T100 final in Qatar where I will also compete again and the Ironman World Championships, and I’ll keep refining the models behind the rankings and predictions. Have a look at the dashboard, dig into the repository, and tell me what you’d like to see next.

Data sourced from PTO Stats (Professional Triathletes Organisation) and Open-Meteo. Results summarised here are selected highlights compiled from public race reporting as of July 2026; see the dashboard for the complete, up-to-date dataset.

Athlete running on treadmill monitored by researchers analyzing performance data on computer screens
Data Analytics, performance, research, Scientific publication, Sports Science

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

The dataset ended up covering 7,415 athlete-race records across 339 events and 3,060 unique athletes, with wet-bulb globe temperature (WBGT) at those races ranging from a mild 7.1°C to a much more challenging 28.0°C.

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.

Analytics, Data, Data Analytics, High Intensity training, Sports Science

What’s New in the Lactate Threshold App: Anaerobic Speed Reserve, Flexible Training Zones, Maximum Speed and More

The Lactate Threshold app started as a simple tool to turn a step test into a clean set of threshold values. Over the past few development cycles it has grown into something closer to a complete profiling and prescription workspace. This post walks through the most significant additions — anaerobic speed reserve, maximum speed determination, switchable 3- and 5-zone models — and the smaller refinements that came with them.

Anaerobic Speed Reserve (ASR)

The headline feature is the ability to determine an athlete’s anaerobic speed reserve — the velocity range that sits between the speed at maximal oxygen uptake (vVO2max, or maximal aerobic speed) and maximal sprinting speed (MSS). Everything an athlete does above their aerobic ceiling happens inside this band, which is exactly why it matters for events decided by surges, kicks and repeated high-intensity efforts.

The concept owes much to the work of Dr Gareth Sandford and colleagues, who showed that ASR and maximal sprint speed are “untapped tools” for differentiating the world’s best middle-distance runners and for understanding the complexity of athlete profiles that traditional aerobic categories miss. Two athletes with an identical vVO2max can have very different reserves above it — and therefore very different tolerances to supramaximal work — information that is invisible if you only look at threshold and VO2max.

Dr. Martin Buchheit’s research extends this directly into programming. Prescribing high-intensity work as a percentage of maximal aerobic speed alone ignores the differing mechanical ceilings between individuals, so the same session can impose very different relative stress on two athletes. Anchoring supramaximal efforts to a percentage of the ASR instead normalises that stress, and the evidence shows it reduces the inter-individual variability of physiological adaptation. The app now makes that calculation a single step rather than a spreadsheet exercise.

Maximum Speed Determination

Because ASR depends on having a reliable upper anchor, the app now supports maximum sprint speed (MSS) determination as a first-class input. Enter the result of a short maximal sprint and the app uses it as the top of the reserve, pairing it with the aerobic anchor derived from the step test. This closes the loop: from a single profiling session you get the threshold values, the aerobic speed, the sprint ceiling, and the reserve that connects them.

Flexible Training Zones: 3 or 5

Training-zone prescription is now configurable. You can choose between a 3-zone model — the classic below-LT1, between-thresholds, above-LT2 structure favoured in polarised approaches — and a more granular 5-zone model for coaches who want finer resolution across the intensity spectrum. Zones are generated directly from the athlete’s own threshold and speed anchors rather than from generic percentages, so the prescription reflects the individual profile the test produced.

Switching between the two models takes a tap, which makes it easy to align the output with whichever periodisation philosophy a given athlete or training block calls for.

Other Improvements

Alongside the marquee features, this round of work brought a number of refinements: cleaner presentation of the threshold detection results, a more consistent workflow from data entry through to zone output, and better handling of the speed-based inputs that the ASR and MSS features rely on. The aim throughout has been to keep the app fast to use rink-side or track-side while quietly adding depth for those who want it.

Development is ongoing, and I’ll keep posting updates here as new capabilities land. If you’re using the app and have feedback or feature requests, I’d be glad to hear them. If you use it for any purposes make sure you reference it:

Cardinale, M. (2026). Lactate Threshold and HIT designer [Web application]. Retrieved from https://lactatethresholdhitdesigner.netlify.app.

⚠️ UPDATE 1st June 2026 ⚠️: I have now added the option for Cycling Tests.

A note for team-sport coaches: if you are specifically after a tool to plan HIIT sessions with change-of-direction (COD) prescriptions, I’d recommend Dr Martin Buchheit’s dedicated COD shuttle prescription app, available here. It is purpose-built for that use case and complements the profiling work the Lactate Threshold app is designed for. A screenshot is below.

Key References

  • Sandford GN, Allen SV, Kilding AE, Ross A, Laursen PB. Maximal Sprint Speed and the Anaerobic Speed Reserve Domain: The Untapped Tools that Differentiate the World’s Best Male 800 m Runners. Sports Medicine, 2019.
  • Sandford GN, Laursen PB, Buchheit M. Anaerobic Speed/Power Reserve and Sport Performance: Scientific Basis, Current Applications and Future Directions. Sports Medicine, 2021.
  • Buchheit M, Laursen PB. High-Intensity Interval Training, Solutions to the Programming Puzzle. Part II: Anaerobic Energy, Neuromuscular Load and Practical Applications. Sports Medicine, 2013.