How Is AI Being Used in Sports Analytics Today?

Sports analysts can use artificial intelligence (AI) to understand players, games, strategies, and fans in new ways. In fact, AI has become such an important 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]. While sports analytics isn’t a new practice, the capabilities of AI models make it possible for analysts to gain a deeper understanding of the complex factors that affect a sports game. 

Sports fans have been collecting data about sports for as long as sports have existed. Sports analytics may have started with operations research, a branch of data analytics born during World War II that focused on understanding and predicting tactical optimizations. Those professionals, either for practice or for fun, started to apply operations research principles to sports teams. With other factors like the rise of sports journalism and the accessibility of information through the internet, sports analytics grew to become an integral part of the game as you know it today. 

Explore how AI is being used in sports analytics to transform the business of sports. This includes helping teams understand their performance and develop targeted training, optimizing strategy, preventing injury, attracting the player with the perfect skill set for a team, and engaging fans in personalized and exciting ways. 

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, as well as what you can do to make improvements. Just like other industries, collecting and analyzing data can allow decision-makers to create informed strategies that 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. 

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 advances in technology, like wearable devices that collect biometric data, also contribute to advancements in sports analytics by allowing analysts access to even more data. 

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 are getting. 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

Not only can AI give players and teams more insight into winning strategies, but it can also 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, and offer analysis about 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. 

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 in a more efficient and fair way. Using AI to comb sources of data, 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 make predictions about 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. 

Learn more about sports analytics on Coursera

Using AI in sports analytics gives coaches, managers, sports medicine professionals, and fans more insight into the game they love, and data that helps them make the best decisions they can with the information they can collect. If you want to learn more about sports analytics or start a career as a sports analyst, you can start learning new skills today on Coursera.

For example, you could enroll in the Sports Performance Analytics Specialization offered by the University of Michigan and learn skills in data analysis, sports analytics, and Python programming.