AI in sports:
a business & technical overview

Coaching & talent development

AI-based performance analysis software provides coaches with valuable insights to develop effective game strategies. AI systems can analyze information gathered via wearable sensors and cameras, including passes, goals scored, rebounds, player’s movement speed, and ball trajectories, to help identify opponents' playing patterns and adjust game plans accordingly. Coaches can even use AI software to run simulations of potential in-game scenarios, test tactical adjustments, and make more informed decisions during matches.

At the same time, AI helps coaches to shed light on their athletes’ strengths and weaknesses and thus create more personalized training programs for performance optimization.

Athlete well-being management

Combined with health wearables, AI-driven systems can monitor biometrics and biomechanics parameters, such as heart rate or muscle loading, to assess a player’s physical condition, spot potential injuries or health problems at an early stage, and thus help physicians select suitable therapies for faster recovery.

These solutions also provide coaches with data-driven insights to develop more balanced training plans, adjusting the intensity and duration of each activity to avoid harmful overtraining and minimize the risk of injury.

Refereeing

Video assistant referee systems (VAR) and similar video-review or instant-replay systems have been used in various sports for years to provide slow-motion highlights to human referees, but they often slowed down the game, creating frustration among fans and players. AI-based computer vision technology, on the other hand, can detect game violations, such as handball offenses in football, way faster and more accurately than traditional systems, minimizing game flow disruptions.

Broadcasting & journalism

Artificial intelligence looks set to influence sports storytelling. Using machine learning and deep learning algorithms, broadcasters can automate numerous video editing operations, including camera shooting and zooming in on key field actions. Fully automated sports production is already almost comparable to professionally edited videos.

Thanks to recent innovations in the field of natural language processing (NLP) and generative AI, automation is also finding its way into sports journalism, enabling the press to create brief news reports for hundreds of matches and cover many more events with fewer resources. Furthermore, media companies can use conversational AI systems to retrieve game footage and other content from large databases through simple queries in natural language. The NFL implemented a similar solution to search for media assets in its vast library of athlete press interviews, fan-generated social media content, and many other resources.

Fan engagement

During sporting events, sports companies can use AI software to provide fans with real-time subtitles in different languages based on their nationality or additional stats and deeper insights to enrich their experience. AI can also be combined with augmented reality to display AI-generated tactical breakdowns via image overlays, or with virtual reality to integrate such insights into 3D environments.

Furthermore, artificial intelligence enables clubs and sports event management companies to offer better customer support via chatbots and other smart assistants. These tools have already been adopted by many sports teams and leagues, such as the NHL and NBA, to address fan questions regarding tickets, parking, and other organizational issues.

Advertising

Offering a great fan experience with artificial intelligence is significantly reshaping sports advertising and, as a result, maximizes advertising commissions. ML-powered marketing solutions analyze viewer preferences, such as demographics, media consumption patterns, and shopping behavior, to segment audiences and deliver personalized, relevant ads instead of generic ones.

Additionally, AI solutions powered by machine learning algorithms can identify the most exciting in-game actions based on fans' emotional response, enabling broadcast companies to better time commercials and capture the attention of their audience for maximum engagement. This makes ads not only more personal but also better timed and more effective.

Betting

Sports betting operators have long shown interest in the predictive analytics capabilities of machine learning systems. So much so that high-tech companies like Sportlogiq have started selling analytical data to bookmakers in the United States, helping them set odds on bets.

However, reliable forecasts would require massive amounts of information, including historical data on individual and team performance, locations, scores, weather conditions, and so on. Many of such details are not public but lie in the hands of sports clubs, so the increasing use of AI in betting leads to an intensification of data trading between different sports-related organizations.