AI in Sport: How Artificial Intelligence Is Reshaping Performance, Officiating and the Fan Experience
- Shane Riddle
- Jun 21
- 8 min read
Artificial intelligence has quietly moved from the sidelines to the centre of elite sport. It now helps coaches read performance, flags injury risk before it lands, speeds up the tightest officiating calls, and even writes the commentary on the highlights you watch afterwards. This is a clear-eyed guide to what AI in sport actually does today, where it's still finding its feet, and why the technology is only as good as the judgement of the people using it. For a closer look at one corner of this story, see our deep dive on the rise of robot officials.
Key Takeaways:
AI is already on the field, not just on the horizon: optical tracking, wearables and injury-risk models are in daily use across elite leagues right now.
Performance analysis got precise: systems such as Major League Baseball's Statcast track the ball and the body to a fraction of an inch, turning raw play into coachable data.
Prevention over cure: load-monitoring wearables and AI risk models help staff manage workload and catch danger signs early informing human decisions rather than replacing them.
Faster, fairer calls: semi-automated offside and ball-tracking systems cut officiating delays while keeping referees firmly in charge.
A richer watch: generative AI now drafts commentary and assembles highlight reels, personalising how fans follow a tournament.
The cautions are real: athlete data privacy, algorithmic bias and transparency are genuine concerns that good governance has to address.
Table of Contents
What we mean by "AI in sport"
"Artificial intelligence" covers a family of techniques that let software learn patterns from data rather than follow fixed rules. In sport, three branches do most of the heavy lifting: machine learning (spotting patterns and making predictions from large datasets), computer vision (reading video and images to track players and the ball), and the neural networks that power both.
Throughout this article we keep one distinction front and centre, because it matters: what ships today versus what is still in development or envisioned. Plenty of breathless coverage blurs the two. The reality is that some AI tools are embedded in daily elite practice, while others are promising prototypes or marketing concepts. We label each honestly as we go.
Seeing the game: tracking and computer vision
The foundation of almost everything else is tracking, knowing exactly where the ball and every player are, many times a second. This is live, mainstream technology.
In baseball, MLB's Statcast has used Hawk-Eye optical tracking since 2020, with twelve high-frame-rate cameras at every ballpark following the ball and player movement to within about a tenth of an inch. The system also reads body pose, limb position, arm angle, stride and since 2023 it has tracked bat speed and swing path on every pitch. Raw play becomes a rich stream of numbers that player-development and biomechanics staff can actually coach from.
The same idea travels off the screen and onto the athlete. GPS and local-positioning vests from companies like Catapult used across the Premier League, NFL and NCAA combining satellite positioning with accelerometers and gyroscopes to measure speed, distance, acceleration and the mechanical "load" a session places on the body. A peer-reviewed review of wearable sensors in sport notes that this movement data is already used to flag which players may be at higher risk of soft-tissue injury which leads neatly to the next section.
Training smarter and preventing injuries
If tracking tells you what happened, AI's real promise is helping decide what to do next, especially around health.
Workload management is the most established use. By comparing today's training load against an athlete's recent history, staff can spot when someone is being pushed too hard, too soon, and adjust before a niggle becomes a tear. This is everyday practice at clubs running wearable programmes.
Injury-risk forecasting goes a step further, and here honesty about the evidence matters. Zone7, an AI platform used by clubs including some in the Premier League and Major League Soccer, builds daily risk forecasts from workload and other data. In a retrospective validation study across eleven professional football teams, the system flagged increased risk in the days before 306 of the 423 injuries that occurred, about 72% on data the model had not seen before. That is genuinely useful, but it is not clairvoyance, a meaningful share of injuries still arrive with no warning, and the higher reduction figures some vendors advertise are club-reported rather than independently verified.
The crucial framing here is these tools inform a human decision, they don't make it. A risk score tells a coach and medical team to look harder at a player; benching a key athlete on a Saturday is still a judgement call made by people who can weigh context the model can't see.
Where each application stands at a glance:
Optical & GPS tracking — Ships today, mainstream in elite leagues: measures ball and body position many times a second.
Workload monitoring — Ships today, widely deployed: manages training load to reduce overuse injury.
Injury-risk forecasting — Ships today, useful but imperfect: flags elevated injury risk before it occurs, with a human making the call.
Semi-automated officiating — Ships today in major competitions: speeds up offside and line calls.
Generative commentary & highlights — Ships today at select events: drafts commentary and builds highlight reels.
Fully automated coaching or refereeing — Emerging / envisioned, not standard: AI making the call or the plan end-to-end.
Faster, fairer officiating
Few areas have changed as visibly as officiating. The headline example is football's semi-automated offside technology (SAOT), introduced at the 2022 World Cup. Around a dozen tracking cameras follow up to 29 points on each player's body 50 times a second, while a sensor inside the ball reports 500 times a second to pin down the exact moment of the kick. The system proposes the offside line automatically; officials then validate it. FIFA's referees committee reported that trials cut the time to reach an offside decision from roughly 70 seconds to about 25.
The technology keeps evolving. For the 2026 World Cup, the system has been refined to send automated audio alerts straight to the assistant referees' earpieces and to flag tighter margins, so flags go up sooner and play stops less often by mistake. Crucially, the human official still makes the final decision, the AI is a fast consistent assistant, not the referee.
This is only one slice of a bigger story. For how line-calling grew from Hawk-Eye in tennis to today's AI-assisted decisions and the live debate about how far automation should go — read our full feature on robot officials and AI refereeing.
A richer experience for fans
AI has also changed how the rest of us watch. At Wimbledon, IBM's watsonx platform has generated AI commentary for highlights videos since 2023, and in 2024 added "Catch Me Up", a generative feature that produces personalised match summaries so fans can pick up what they missed. The highlight reels themselves are assembled by AI that reads crowd noise, player gestures and match data to find the most exciting moments, work IBM has been refining since 2017.
For fans, the practical upshot is content that fits you: reels and summaries built around the players you follow, captions that improve accessibility, and analysis that used to require a studio full of producers. It's the same data that powers the coaching side of the game, turned outward toward the sofa and the stadium seat. (Augmented reality is pushing this further still which is a topic we explore in our sports technology overview.)
The honest limits: data, bias and the human in the loop
Putting my IT Governance hat on, there are a few items for us to consider as we introduce this technology. The first is athlete data. These systems run on detailed biometric and performance information, some of the most sensitive data a person has. A systematic review of the ethics of AI in sport found privacy and data ethics to be the single most-discussed concern across the literature, alongside fairness, transparency and accountability. Who owns an athlete's data, who can see it, and whether a player can meaningfully consent are live questions, not settled ones.
The second is bias. A model only knows the patterns in the data it was trained on. In talent identification especially, an analysis of AI and human rights in sport warns that systems can narrow toward a single profile of "talent" and quietly disadvantage athletes who don't fit it, a particular risk for young players who can't easily consent to how their data is used.
The third is explainability. When a risk score or a recruitment recommendation can't be explained, it's hard for an athlete to trust it or challenge it. The strongest deployments keep a person accountable for every consequential decision.
There is, notably, no single body that governs AI in sport. Standards are set piecemeal by competition organisers and lawmakers such as FIFA and the leagues, by the technology vendors themselves, by general data-protection law, and by a still-emerging set of sport-specific ethics frameworks. For readers and clubs, that makes asking good questions about consent, accuracy and oversight part of using these tools responsibly.
Where AI in sport is heading
Looking forward and clearly labelling this as emerging rather than established, a few directions stand out. Commentary systems could pair language models with computer vision to describe not just the score but the style of a rally or a run. AI coaching assistants that suggest drills and tactical tweaks are in development, though they remain aids to human coaches rather than replacements. And generative tools are beginning to build personalised, interactive fan experiences in real time.
The throughline is collaboration, not substitution. The most credible vision of AI in sport as I see it isn't a robot taking the field, it's better information reaching the people who play, coach, officiate and watch so that the human moments that make sport worth caring about shine a little brighter.
Final Thoughts
AI hasn't replaced anything essential about sport. The contest, the craft, the drama, all of it still belongs to people. What artificial intelligence has done is sharpen the lens measuring performance more precisely, catching injury risk earlier, settling tight calls faster, and bringing fans closer to the action. Used well, with honesty about its limits and care for the people whose data feeds it, AI is one of the most powerful tools sport has ever had for helping athletes thrive and audiences connect. The job now is to keep the human firmly in the loop and to keep asking who the technology is really serving.
Frequently Asked Questions (FAQ)
Q: Is AI actually used in professional sport today, or is it mostly hype?
A: Both, depending on the tool. Optical tracking (such as MLB's Statcast), GPS wearables for load monitoring, injury-risk forecasting and semi-automated offside are all in real, daily use at elite level. Fully automated coaching or refereeing, by contrast, is still emerging rather than standard.
Q: Can AI predict injuries?
A: It can forecast elevated risk, not guarantee an outcome. In one validation study across eleven professional football teams, an AI platform flagged increased risk before about 72% of injuries. That's valuable for managing workload, but a meaningful share of injuries still occur with no warning, and the tools support human medical judgement rather than replacing it.
Q: Does AI make officiating decisions on its own?
A: Not in mainstream competitions. Systems like football's semi-automated offside technology do the measuring and propose a decision, but a human official validates and makes the final call. It speeds things up and improves consistency while keeping people accountable.
Q: What are the main risks of AI in sport?
A: The most discussed concerns are athlete data privacy, algorithmic bias (especially in talent identification), and a lack of transparency in how decisions are reached. There's no single governing body for AI in sport, so responsible use depends on good practice around consent, accuracy and human oversight.
Q: How does AI change things for fans?
A: It personalises the experience, AI-generated commentary, automatically assembled highlight reels, and summary features that let you catch up on the players and matches you care about, often with improved accessibility through captions.
