From tracking the trajectory of a footballer’s strike to predicting injury risks before they happen, the athlete data business is undergoing a seismic shift. Powered by artificial intelligence and next-generation sensors, the industry is moving beyond simple statistics to deliver insights that were unimaginable just a few years ago.
The numbers tell a compelling story. The global sports analytics market, valued at $4.25 billion in 2025, is projected to skyrocket to nearly $104 billion by 2035, growing at an extraordinary rate of 37.68 per cent . Meanwhile, the narrower AI in sports market is expected to reach $5.01 billion by 2034, up from $1.43 billion in 2026 . Behind these figures lies a fundamental transformation in how athletes train, how coaches strategise, and how fans engage with their favourite sports.
From Clipboard to Cloud: The Data Explosion
The journey from the “Moneyball” era of baseball statistics to today’s AI-powered insights represents a quantum leap in sophistication . Where once analysts pored over box scores and batting averages, modern sports organisations now swim in oceans of data. The NFL’s “Digital Athlete” platform, built with AWS, captures approximately 500 million data points per week—more than entire seasons generated in the past .
“We’ve moved beyond simply counting what happens,” explains Arnaud Santin, co-founder of the Britain-based start-up SportsDynamics. “This very reliable data lets you recreate 100 per cent of what is happening on the field, without just following the ball around” . His company offers a Silicon Valley-inspired software-as-a-service model that allows clients to analyse not only their own players but also those of any opponent, processing up to 50 images per second during big games .
The technology stack driving this revolution is diverse. Wearable performance sensors track speed, breathing rates, and cardiovascular readings. Stadium cameras monitor every player’s movement, whether they have the ball or not. And increasingly, artificial intelligence ties it all together, finding patterns invisible to the human eye .
Markerless Motion Capture: The Smartphone Revolution
Perhaps nowhere is the technological leap more dramatic than in the recent collaboration between U.S. Ski & Snowboard and Google Cloud. Together, they have developed an industry-first AI video-analysis tool that turns standard smartphones into high-precision motion capture devices .
For decades, elite winter sports coaching faced an impossible choice: subjective human observation on the mountain or high-precision data inside a laboratory. Traditional motion capture requires athletes to wear specialised suits covered in sensors—impractical for outdoor training and prone to failure in sub-zero, high-velocity conditions .
“This new AI tool is a major coaching development,” says Anouk Patty, chief of sport at U.S. Ski & Snowboard. “Video is the most commonly used and effective coaching tool, but analysing it used to be a manual, time-consuming process” .
The breakthrough lies in “markerless motion capture” technology from Google DeepMind, which can map a human body in 3D using only video. The AI learns to “see” through bulky winter gear, identifying skeletal points through clothing and equipment without requiring any wearable sensors . For athletes like snowboarder Maddie Mastro, this means receiving detailed biomechanical analysis of tricks like her signature double crippler simply by handing a phone to someone at the base of the half-pipe .
Conversational
The U.S. Ski & Snowboard tool represents another frontier: natural language interaction with performance data. Powered by Google’s Gemini multimodal AI, coaches and athletes can now query their footage conversationally .
“Instead of scrolling through spreadsheets, a coach can simply ask the tool, ‘Based on that airtime, how much faster did the rider need to spin to complete the rotation?'” explains Oliver Parker, vice president of Global Generative AI at Google Cloud. The AI then estimates the angular velocity needed to land the trick successfully .
This conversational layer democratises access to complex data. Olympic silver medallist Colby Stevenson describes the experience: “Just the ability to know how different some of these tricks are, and maybe it’s the same trick, it feels the same, but then have all these different data points tell you it isn’t—it’s interesting. And then to be able to ask, ‘OK, what felt different about that? What made it come out with different numbers?'”
Beyond Performance: Injury Prevention and Safety
While enhancing performance drives much of the innovation, injury prevention may represent an even more valuable application. The ability to detect subtle changes in movement patterns that precede injury could extend careers and save organisations millions .
“We’re in a sport where there’s fear and other elements happening,” says snowboarder Maddie Mastro. “To be able to have a little validating reminder of data, like—no, you’re doing this right, you’re doing this safe, your mechanics are good, you’re ready for that next step—is sometimes necessary” .
The stakes are high. U.S. Ski & Snowboard’s Patty notes that with more than 240 athletes, 40 to 50 can be sidelined by injuries at any given time. “What Google’s tools can do for us, we’ve talked about the importance of the skeleton and being able to see where they’re leaning too much on one side versus the other… Being able to see that will really help the coaches to sort of mitigate some of the injuries that we sometimes face” .
The Business of Data: From Betting to Broadcasting
The commercial potential of athlete data extends far beyond team performance. Specialised firms are finding lucrative opportunities in providing content to broadcasters, enticing fans to online betting markets, and keeping audiences engaged during off-seasons .
Germany-based Data Sports Group uses live television coverage of sports including rugby and cricket to provide content to media clients, gaming companies, and fantasy sports providers. For bookmakers, the company offers “statistics and reference material over a period of archives, so they can take decisions on that,” says business director Rajesh D’Souza .
The market’s potential has not gone unnoticed by investors. In February, American data specialist Genius Sports announced a $1.2 billion deal to acquire betting and gaming content platform Legend—a clear signal of expectations for explosive growth .
Market Trajectory: Europe Catches Up
While North America has traditionally led in sports analytics adoption, Europe and Asia are rapidly closing the gap. Consulting firm EY’s Lodovico Mangiavacchi notes that “reports forecast that the European sports analytics market will swell to multibillion-dollar size over the coming decade,” with one study from Market Research Future predicting it will reach $7.5 billion by 2032 .
“Behind these numbers lie investments in wearables, sophisticated video analysis tools, and Internet of Things devices,” Mangiavacchi adds .
Football, as Europe’s dominant sport, is driving much of this growth. The English Premier League’s Brighton & Hove Albion offers a compelling case study in AI-powered recruitment. Owner Tony Bloom’s secret algorithm system has enabled the club to identify undervalued players, buying Moisés Caicedo for €10 million and later selling him to Chelsea for €110 million—a tenfold return on investment .
The Democratisation of Elite Coaching
One of the most promising aspects of AI in sports is its potential to level the playing field. “I think artificial intelligence will promote competitive balance,” says Juan Manuel Alonso, author of a study on AI’s impact on elite sports. “Teams or athletes with fewer resources will be able, thanks to this technology, to achieve goals that previously could only be reached with large human teams” .
This democratisation extends beyond professional sports. Google Cloud’s Parker envisions a “global shift in how humans move, train, and recover, moving beyond historical data to provide athletes with near real-time, prescriptive coaching. By using our full-stack AI, we’re helping democratise elite coaching—proving that if we can solve for the world’s best athletes in the most extreme conditions, we can help anyone from a physical therapy patient to an amateur golfer improve their games” .
Challenges: Data Ownership and Privacy
The explosion in athlete data raises important questions about control and privacy. In Europe, such information falls under the General Data Protection Regulation’s strict requirements . However, as SportsDynamics’ Santin points out, “professional athletes, in the majority of cases, sign a contract that allows their clubs and the league to use their data” .
Data quality presents another challenge. AI algorithms depend on large, high-quality datasets to learn and make accurate predictions. Obtaining comprehensive, standardised data remains difficult in some sports or for specific performance metrics . Additionally, as data volumes grow, so does the need for investment in security to protect against theft .
The Future: From Descriptive to Prescriptive
Industry experts see AI’s role evolving from descriptive—what happened—to prescriptive—what should be done about it. Frank Imbach, a director of French group SeeSports, summarises the value proposition: “When a professional club or federation has data on their players, we can analyse it and make recommendations on how to optimise their performance or avoid an injury” .
As the technology matures, its applications will likely expand beyond elite sport. What begins with Olympic athletes training in extreme environments may eventually benefit physical therapy patients, amateur golfers, and weekend warriors . The same AI that helps a snowboarder perfect a 1080-degree rotation could one day help a runner recover from knee surgery or a golfer correct their swing.