Sports analytics has always been based on structured data. Coaches, players, and betting sites use it to track trends, develop strategies, and estimate outcomes. But new developments in artificial intelligence are starting to alter the way that data is gathered, interpreted, and applied.
The Role of Data Before AI
Long before the use of AI tools, analysts, teams, and platforms built systems that relied on human review. In football or basketball, video analysts stopped footage and reviewed plays, recording every individual action of a player. Sports betting companies utilized statistics from the leagues and other publicly available sources.
These were generally packaged and sold by data providers that specialized in collecting data for performance history, team comparisons, or player form. Coaches would use the same data to see where their team needed to change.
Betting places particularly relied on fixed and reliable data. Since odds are calculated using past results and public trends, the sources had to be complete and updated frequently. Industry insights, like the full list on Readwrite.com, offer users a way to explore which data platforms and betting sites were the most consistent.
In the model at the present time, that process still takes time. The more steps it takes to collect, verify, and apply the data. With AI, such insights could get even better and more accurate.
How AI Tools Are Being Used in Real Events
Olympic Broadcasting Services used it for behind-the-scenes cuts and edits, to improve workflows, and hone the way events got covered.
These systems helped to enhance the way that highlights were generated, footage was sorted, and stories were presented to the audience. The technology made it easier to match the expectations of the viewer with real-time analysis of what was happening during the games.
In the field of gymnastics, the International Gymnastics Federation collaborated with Fujitsu in the development of the AI-assisted Judging Support System (JSS). This system used cameras to trace the movement of gymnasts in 3D. It matched those movements compared to a database of known elements and gave judges accurate suggestions for scoring.
That helped to make the judging process more consistent and minimize human error. These examples illustrate the practical application of AI not as a predictive tool but as part of the live event.
Athlete Training and AI-Generated Self-Analysis
Some athletes have used AI tools in their own training and recovery process. Digital trackers were used by cyclist Samantha Bosco in her preparation for the Paralympic Games. She recorded the sleep patterns, monitored her nutrition, and analyzed the results with the aid of AI tools.
These insights became a part of the system that supported her gold medal win at the 2024 Paris Paralympic Games. Weightlifter Jourdan Delacruz used Microsoft Copilot to plan her diet and training when she ran her second Olympic run. She searched for meal ideas and systematized her personal performance data.
This tool helped her to find better ways to recover and be on track. These stories demonstrate that the application of AI is not in labs anymore or in tech companies; it is already in the hands of professionals during real preparation. The same tools are also being used by analysts working with Parity, a group that helps women athletes close the gap in sports data access.
How AI Is Reshaping the Fan Experience Through Data
AI is starting to change what fans expect when they attend a game. It’s not only about what happens on the field. Teams are using data to shape everything from how fans enter the stadium to how they stay engaged after the final whistle.
Some teams now use AI-powered VR setups to draw fans closer to the game. The Golden State Warriors built immersive experiences that simulate live courtside views, helping fans feel part of the action from home.
Off the field, AI works through apps and digital platforms. It curates content based on past fan behavior. One fan may see player highlights. Another may get ticket offers or discounts that match their habits. Chatbots now handle player updates, ticket searches, and general questions, giving fans quick answers without needing to call or wait.
Others focus on movement and access. The Los Angeles Dodgers use AI-assisted facial recognition to match fans with their ticket data. This helps fans enter the stadium faster and find their seats without needing extra help.
Inside the venue, AI helps manage traffic. It tracks movement in real time and flags crowd buildup, which allows teams to adjust flow before it turns into a problem. Teams also use biometric tools to measure fan excitement. These systems check for heart rate spikes to learn which parts of the game trigger the strongest reactions. Together, these tools turn passive viewing into a connected experience.
What AI Might Be Able to Do Next in Sports Analytics
Looking to the future, AI tools could be used to train referees, watching hundreds of scenarios from the match and focusing on decisions that are outside of accepted standards. The sports AI market is expected to reach $19.2 billion by 2030, and a growing share of that investment is going into fan experience.
Teams are not only using AI for player analysis. They’re using it to change how fans watch, interact, and respond to games. That data can shape future content and in-stadium features. That data can shape future content and in-stadium features.
On the court, referees could then go through side-by-side comparisons of similar situations and learn where calls go wrong. This would help unify the decision-making across matches, leagues, and even different sports. With the amount of video available from professional and amateur games, there is enough video there to build large datasets that AI systems can train against.
AI may also be of assistance in environmental and location-based analysis. In outdoor sports like cricket or tennis, the weather conditions may play a huge role in the course of the game. AI could monitor the effect of humidity, temperature, and the type of surface on player performance and then correlate these trends to forthcoming fixtures.
This type of prediction could be used to support team preparation or planning of a venue.
