How Is AI Being Used in Sports Analytics Today?

Key takeaways

Sports analysts can use artificial intelligence (AI) to understand players, games, strategies, and fans in new ways.

• The global AI in sports market is growing, with a projected value of $29.7 billion by 2032 [1]. 

• Wearable AI devices can track and analyze player performance, helping sports medicine professionals predict and prevent injuries. 

• You can also use AI in sports for data analysis, helping to analyze player performance and track recruitment efforts.

Explore how AI is being used in sports analytics to transform the business of sports. If you’re ready to build your skills, consider enrolling in the Sports Performance Analytics Specialization. In as little as four months, you’ll have the chance to understand the science behind athlete performance and game prediction. 

What is sports analytics?

Sports analytics applies data analytics principles to sports, collecting and understanding data to gain more insight into how and why things work and what you can do to make improvements. Like other industries, collecting and analyzing data can allow decision-makers to create informed strategies to help their organization meet its goals. Whether the decision-maker is a coach who wants to create the best training program possible for their players, a marketing director who wants to fill the seats in the stadium for every event, or a sports medicine professional who wants to make sure athletes stay safe and healthy, data can provide the insight needed to make decisions with confidence. 

AI has become such an essential part of the sports industry that Allied Market Research projects the global market value of AI in sports will be worth $29.7 billion by 2032, growing at a compounded annual growth rate of 30.1 percent year over year [1].

Before advances in AI, sports analysts used a variety of mathematical and statistical analyses to drive decision-making. AI can make that process easier, faster, and more accurate. AI allows sports analysts to react to data in real-time and make decisions based on data that would otherwise have to wait for analysis, giving analysts an important advantage in the dynamic environment of a game. Other technological advances, like wearable devices that collect biometric data, also contribute to advancements in sports analytics by allowing analysts access to even more data. 

Read more: On-Device AI: Powering the Future of Computing

AI in sports: How is AI being used in sports analytics today?

Professionals at every level of the sports industry can use AI to make better decisions and improve performance. A few examples of how sports analytics uses AI today include improving player performance, optimizing game strategy, preventing injury, increasing engagement, and targeting recruitment efforts. 

Using AI for player performance analysis

AI allows sports analysts to understand player performance like never before. Using historical data about how players performed in the past, wearable sensors and devices that measure data in real-time, and computer vision devices like cameras that can track the movements of a ball, AI allows coaches and managers to gain insights they can use to make real-time decisions in the heat of a match, create training programs tailored to the skills and physical health of individual players, and create predictive models to guess which players are about to have a great season and which players are at risk for an injury. 

Using AI for game strategy optimization

AI makes it easier to optimize game strategy by analyzing a huge amount of data, including past performance on both a team and individual level, and combined with information about the environment, terrain, and weather. This wealth of data, from the level of aggression players demonstrate on the field to the exact positioning of each player as they move, can help coaches and managers determine strategic moves that will help them overcome their opponents. For example, data could demonstrate when the optimal moment is to substitute a player as well as which player on the bench would be most beneficial to enter the dynamic on the field. AI is capable of finding patterns in this data that may not be immediately obvious to a human sports analyst. 

Using AI for injury prevention and rehabilitation

The same information you can use to predict player performance, like biometric and historic data, can help sports medicine professionals predict and avoid player injuries. When players are performing, coaches and managers can monitor their exertion levels and the body mechanics of their movement to tailor training sessions and help limit the risk of injury. In addition to tracking data when players are playing in a game or during practice, medical professionals can collect other biometric data that contributes to performance, such as fatigue levels or how much sleep players get. In the event a player does experience an injury, AI can help sports medicine professionals tailor rehabilitation efforts to the player’s needs and monitor their recovery with greater precision. 

Using AI for fan engagement and experience

AI can give players and teams more insight into winning strategies and offer fans a more engaging experience in and outside the arena. AI can display dynamic historical data or trivia when players enter the field. For example, during a baseball game, you might expect to see a player’s batting average when they come up to bat. 

AI could go a step further and offer statistics about the player’s batting average against the exact pitcher opposing them at the mound, as well as an analysis of how that compares with the player’s batting average overall. AI can also offer better fan experiences inside the stadium, such as an AI chatbot that can help guests find the bathroom or other features of the arena. The technology can help create immersive virtual reality experiences for fans to feel involved in the action of a game in a new way. 

How do NFL teams use AI?

NFL teams use AI, including Microsoft Copilot, on the sideline to access real-time game data and gain insight into scoring plays, downs, distance, and penalty yards. Furthermore, AI assists with player scouting by analyzing game film to help teams decide who to draft. AI extends into NFL front offices as well, helping teams make data-driven business decisions based on AI-powered insights. 

Using AI for data-driven recruitment

Another way professionals in sports use AI is to make data-driven decisions about recruitment. AI can help recruiters scout from a wider variety of places more efficiently and fairly. Using AI to comb through data sources, recruiters can identify players who aren’t getting the same media attention as their colleagues but who demonstrate a lot of potential in their craft. AI can also predict how players will work together on the field, giving coaches more insight into how to manage the talent on their team and informing choices about trades and other player management decisions. 

Discover free AI and data analytics resources

Subscribe to our LinkedIn newsletter, Career Chat, to gain access to industry trends and career tips. Then, check out the following resources to learn more about AI and data analytics:

• Learn the terminology: Artificial Intelligence Glossary: Learn AI Vocabulary 

• Watch on YouTube: AI Quick Tip: Data Analytics 

• Hear from a pro: AI Creativity Unleashed: Expert Insights from Vanderbilt’s Dr. Jules White

Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses. 

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial