Tag: Open data

Glowing human skeleton, medical dashboard, and robotic arm amid scattered technical documents
Artificial Intelligence, Education, Sports Science, Technology

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.

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.

data analysis, Football, Injuries, performance

When FIFA Opened the Data: How the World Cup Is Changing the Way We Understand the Game

For most of my career, the richest data in elite football sat behind closed doors. Tracking systems, multi-camera optical feeds, possession context, off-the-ball movement — the kind of information that actually explains why a match unfolds the way it does — was the private property of a handful of federations and clubs wealthy enough to own it. Everyone else worked with shots, corners, fouls and possession percentage, and squinted to fill in the rest.

That is changing, and the FIFA World Cup has become the most visible stage for the shift. Over the last two tournaments FIFA has done something I think is genuinely important for our field: it has taken some of the most modern performance data ever produced in football and made a meaningful slice of it public. As someone who has spent years arguing that data only becomes knowledge when people are allowed to interrogate it, I find this exciting — so I wanted to write down where this data lives, who is doing interesting things with it, and why the act of sharing matters as much as the numbers themselves.

FIFA’s initiative: Enhanced Football Intelligence

The centrepiece is Enhanced Football Intelligence (EFI) — the set of metrics that first appeared as those small graphics in the corner of the screen during the 2022 World Cup. EFI was built by FIFA’s Football Performance Analysis & Insights team to move us beyond traditional counting stats and toward metrics that describe how a team plays.

What makes EFI different is its source. Rather than relying only on on-the-ball event data, it combines event data with live tracking data from every player on the pitch, captured by a multi-camera optical system. When something happens, you know where all twenty-two players were in relation to it. That positional context is what unlocks metrics you simply cannot derive from a traditional stats sheet, including:

  • Line breaks — how often a pass cuts through an entire defensive unit, and whether it went through, around or over. FIFA’s own analysis shows that the more line breaks a team concedes in midfield, the more games it tends to lose.
  • Ball recovery time — how long, on average, it takes a team to win the ball back after losing it.
  • “In contest” possession — the honest third category that sits between “our ball” and “their ball,” capturing the messy phases when nobody is truly in control.
  • Receptions behind the midfield and defensive lines — where and how players make themselves available between the opponent’s units.
  • Pressure on the ball and forced turnovers — whether a side is genuinely disrupting the opponent or merely looking busy.

Crucially, FIFA makes this available to the public. The metrics are explained — with video and multilingual PDFs — on the FIFA Training Centre, and snippets of match data are published after games. For the 2026 tournament FIFA has gone further still, layering on two new initiatives: the FIFA Power Rankings, an objective player-rating system scoring every outfield player 0–10 for attacking, creativity and defending using EFI algorithms; and FIFA AI Pro, which gives all 48 teams the same generative-AI tools to explore match data and rebuild moments in 3D — explicitly framed as democratising analytics that used to belong only to the biggest budgets.

Where to find the data and the people working with it

Here are the sites I’d point any coach, analyst or curious fan toward. I’ve grouped them deliberately, because one distinction matters a great deal and is easy to miss: some of these work directly from data officially published by FIFA, while others produce excellent World Cup analysis using third-party providers such as StatsBomb. All are worth your time — but knowing which is which keeps you honest about where a number actually came from.

The official source

FIFA Training Centre

This is the well from which everything else is drawn. Free, open, and aimed at coaches of every level, it hosts the EFI metric explainers, the “Football Language” glossary, video breakdowns, the FIFA Insight interviews with the people who built EFI, and — the part I’d flag for 2026 — the Match Report Hub, a live index of post-match summary reports for every World Cup match, organised by group and added as the tournament unfolds. If you only bookmark one link from this article, make it this one.

Working directly with FIFA’s published reports

Tactics Journal

The sharpest example I’ve seen of someone treating FIFA’s openness as a system rather than a curiosity. Tactics Journal points out that FIFA is publishing a roughly 52-page post-match summary for every group-stage game — formations, pressing phases, line-break tables player-by-player, defensive pressure maps, sprint-zone physical data — all in a consistent structure. Their argument is that because every report follows the same layout, you can parse all of them into a structured, auditable evidence layer for scouting and tournament-wide tactical questions. They call it “tactical infrastructure,” and I think that’s exactly the right way to think about it: the PDF is content; the structured layer you build from it is infrastructure.

EFI Data Reference

An independent blog that does something quietly useful: it organises and explains the EFI metrics as a reference, pulling together the definitions of phases of play, line heights, team lengths, receptions and the rest into one navigable place. A patient companion to the official material that helps a newcomer go from “what is a line break?” to actually reading a match through that lens.

Doğan Parlak’s open-source EFI implementation

My favourite example of why open methods matter as much as open data. Parlak built an open-source implementation of FIFA’s EFI metrics — data, concept and visualisation layers — with the explicit goal of reproducing FIFA’s match reports and testing whether the published concepts are specified well enough to be rebuilt by an outsider. That is science in the best sense: take the published method, try to recreate it, and flag the ambiguities. For analysts and students it doubles as a practical toolkit for generating EFI-style visualisations. You can read his Master’s thesis on this project here.

The wider analytics and data-journalism ecosystem

These don’t all run on FIFA’s own feed, but they show what a culture of shared football data makes possible — and they’re some of the most engaging World Cup analysis being published right now.

Northeastern Global News — NGN Offside / NetSI Sport

A blog “powered by data science and written by journalists,” produced by Northeastern’s Network Science Institute (the NetSI Sport group led by Brennan Klein). It’s a masterclass in turning event data into narrative: passing networks and passing-cluster maps that fingerprint a team’s style, xG shot maps, dribble-and-carry graphics, and genuinely novel angles like whether the 2026 hydration breaks are changing scoring patterns. Worth knowing that their underlying data comes from Hudl StatsBomb (over 3,400 events per match), not FIFA’s EFI feed — a good illustration of how the official and commercial data worlds sit side by side.

Datawrapper — Data Vis Dispatch

Not a football site at all, but a weekly roundup of the best data visualisations from newsrooms around the world, and during the tournament it’s been a reliable showcase of World Cup charts — qualification journeys, the evolution of the match ball, player and game analyses from the likes of Reuters, The New York Times and El País. The best place to see how professional data journalists choose to present this kind of information. Some details are also here.

Microsoft Fabric Community — FIFA World Cup 2026 Stats Analysis Hub

A reminder that you don’t need a newsroom to do this. This is a community-built interactive Power BI dashboard — one example aimed at World Cup Fantasy players, with a “Stat View” toggle to compare a player’s club versus international form before making transfer decisions — sitting within Fabric’s wider Data Stories Gallery of user-made World Cup dashboards. A nice window into the grassroots, build-it-yourself end of the spectrum.

(Two more worth a look in the same spirit: Tactical Football Analysis for written post-match tactical breakdowns, and Flourish, whose football chart templates are a quick way to build your own tournament visualisations.Also, have a look at this substack on how to build team-shape visualisations.)

What the analysis actually looks like

The reason this data is worth sharing is that it produces visuals that change how you see a match. A few of the workhorse formats:

  • Pass networks map who connects to whom and where, turning a team’s structure into a readable shape — you can see at a glance whether a side is building through its full-backs, overloading one flank, or bypassing midfield entirely.
  • Tracking heatmaps show where players and teams actually spend their time, exposing the difference between nominal position and real behaviour, and revealing how compact or stretched a side is in and out of possession.
  • xG (expected goals) shot maps put a probability on every chance so you can judge whether a team created genuine danger or simply accumulated low-value shots — useful, as long as you remember it’s a guide, not a verdict.

The principle underneath all of them is the same one I keep coming back to: the best statistics don’t make football more complicated, they make it easier to understand. A line-break count, a compactness number or a clean sprint map isn’t there to replace the coach’s eye — it’s there to sharpen the question the coach is already asking as well as providing additional information for support staff to improve how players are prepared.

The other half of the story: injuries and player health

It would be a strange omission, for me especially, to write about World Cup data and talk only about what happens when players are on the pitch. The same tournament that generates all this performance data also generates a great deal of information about the cost of playing it — and FIFA itself frames its data mission broadly, as unlocking the potential of video and data to drive technical development and education, not just tactical analysis.

This World Cup has put player load firmly in the spotlight. The expansion to 48 teams and 104 matches, layered on top of an already congested club calendar, has prompted warnings from sports-medicine specialists that fatigue and tight scheduling are pushing injury rates up — with the knee particularly exposed to the constant cutting, pivoting and rapid changes of direction the modern game demands. FIFA’s own venue medical staff have pointed to the injuries they see most often in elite players: ankle sprains, and hamstring and calf strains. None of that is new in kind, but the volume and the schedule are. At the end of each tournament there is always a published paper on the injury surveillance activities conducted by the FIFA medical team. If you want to read more about our experience at the FIFA 2022 World Cup you can read the following papers:

Serner A, Chamari K, Hassanmirzaei B, Moreira F, Bahr R, Massey A, Grimm K, Clarsen B, Tabben M. Time-loss injuries and illnesses at the FIFA world cup Qatar 2022. Sci Med Footb. 2025 Aug;9(3):275-282. doi: 10.1080/24733938.2024.2357568. Epub 2024 Jun 11. PMID: 38860817.

Schumacher YO, Kings D, Whiteley R, Dharman A, Taqtaq G, Mc Court P, Alkhelaifi K, Targett S, Holtzhausen L, Pieles GE, Dzendrowskyj P, Zikria BA, Bordalo M, Al Hussein I, D’Hooghe P, Al-Kuwari A, Cardinale M. Medical services at the FIFA world cup Qatar 2022. Br J Sports Med. 2023 Oct 27;58(1):42–9. doi: 10.1136/bjsports-2023-106855. Epub ahead of print. PMID: 37890964; PMCID: PMC10804010.

Bordalo M, Serner A, Yamashiro E, Al-Musa E, Djadoun MA, Al-Khelaifi K, Schumacher YO, Al-Kuwari AJ, Massey A, D’Hooghe P, Cardinale M. Imaging-detected sports injuries and imaging-guided interventions in athletes during the 2022 FIFA football (soccer) World Cup. Skeletal Radiol. 2025 Apr;54(4):819-828. doi: 10.1007/s00256-023-04451-z. Epub 2023 Sep 16. PMID: 37715819; PMCID: PMC11845536.

Bordalo M, Evans T, Allenjawi S, Targett S, Dzendrowskyj P, Al-Kuwari AJ, Cardinale M, D’Hooghe P. Management of radiology services during the 2022 FIFA football (soccer) World Cup. Skeletal Radiol. 2025 Apr;54(4):647-653. doi: 10.1007/s00256-023-04486-2. Epub 2023 Nov 9. PMID: 37943308; PMCID: PMC11845430.

Alsenoy KV, Raisi LA, Shamsi FA, Thomson A, D’Hooghe P. Service Planning and Provision During Qatar’s 2022 FIFA World Cup and 2023 AFC Asian Cup: Sports Podiatry. J Am Podiatr Med Assoc. 2025 Sep-Oct;115(5):24-176. doi: 10.7547/24-176. PMID: 41166158.

For the public, the most visible “injury data” is the running tracker — outlets such as ESPN, The Independent and others maintain live lists of who is ruled out or racing to be fit, and this tournament has tested several squads hard, with sides like Brazil and the Netherlands losing first-choice players before a ball was kicked. These are journalism rather than open datasets, but they perform a real function: a structured, continuously updated public record of availability.

FIFA has a long history of medical research around its tournaments, and the combination of tracking data (sprint loads, high-intensity distances, accelerations) with injury records is exactly the kind of linkage that could move us from counting injuries to understanding and preventing them. The performance data and the medical data are two halves of the same picture; sharing both is how we protect the players who generate it.

Why sharing the data matters

I want to close on the part I care about most, because it’s easy to treat “FIFA released some metrics” as a minor technical footnote. It isn’t.

It democratises insight. When tracking-derived data was private, the gap between the richest and poorest programmes was partly a data gap. Publishing EFI, and giving every World Cup team the same AI tools, narrows that gap. Insight stops being a function of budget alone.

It creates a shared language. When a coach in one country and an analyst in another can both point to the same definition of a line break or “in contest” possession, conversations get more precise and more productive. Common metrics are the grammar of a common discussion.

It invites scrutiny and reproducibility. The moment a method is public, people like Doğan Parlak can try to rebuild it, stress-test it, and improve it. That is exactly how a field matures — not by guarding methods, but by exposing them to challenge.

It stimulates new ways of analysing the game. This is the part that excites me as a scientist. Hand the same dataset to a hundred curious people and you will get analyses no single organisation would ever have commissioned. Open data is generative: it produces questions, tools and visualisations that wouldn’t otherwise exist, and the elite game gets smarter as a result.

Football has always been understood through stories and through the eye. What FIFA’s data initiative does is add a third lens — a transparent, shareable, contestable one — and then, remarkably, hand it to everyone. The numbers are interesting. But the decision to share them is what will change how we understand the game at the very highest level. So, well done to my FIFA colleagues for this initiative.

At Aspetar we have produced a special issue of our Journal dedicated to Football and the World Cup. You can access it by clicking on the cover page.