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Artificial Intelligence and Machine Learning Technologies in Sports

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  • Difficulty Level : Easy
  • Last Updated : 23 Jan, 2023
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From Cricket to Formula 1, AI and ML are used in sports to make strategy, training, advertising/marketing, and do much more. AI is making a digital world for sportsmen, advertisers, broadcasters, with real-time statistics. AI is changing the ways of doing business, and while its influence has been prominent in several industries already, the Sports sector is a new member and a very welcoming one as well. Artificial Intelligence is flourishing in this domain for the last few years. Statistics have always played a main role in the sports world and AI has significantly impacted the level of audience interaction, strategic plan implementation, etc.

Applications of AI and ML Technologies in Sports

1. Augmentation and Player Performance Improvement

Wearable AI technology like Sensors and high-speed cameras measure Leg Before Wicket (LBW), forward pass, penalty kicks, and other similar actions in various sports effectively. With the help of AI, players can study and prepare well for competitions. The data-driven analysis of players helps the coach to develop better training sessions for their teams. Once the coaches know where their players lag, it becomes easier for them to train.

2. Sports Journalism

Natural Language Programming (NLP) and AI have increased the strength of Sports Journalism. With the help of AI, journalists can analyze a huge amount of data and make predictions on specific topics. Also, journalists can focus on important matters and can automate their daily and time-consuming routine. Also, AI can use algorithms to stand against fake news.

3. Virtual Reality

Virtual Reality in sports makes us able to view a play from any angle. VR has revolutionized the way we watch sports and has started attracting more new viewers. With the help of VR, viewers can see their favorite player in the match. VR technology is making a real viewing experience. Virtual reality has made sports gaming to become more immersive.

4. Match Predictions

Machine Learning is used to predict match predictions. Sports like cricket and football’ have a large amount of data and outcomes can be created using these technologies. It enables model-building based on copious amounts of data without explicit commands. Machine learning application is using deep neural networks along with artificial neural networks to predict outcomes. An app named Kick-off can predict the probability of winning sides of both sides. Swarm AI Technology uses a hybrid AI where a people network analyzes the match forecast.


  • Cricket – AI helps to enhance the match scenario by improving the match results obtained with accuracy. Above all, its help within the right prediction of batting average, bowling average, runs gain, and centuries within the whole tournament is entirely new. In cricket, AI is also used in the Umpire Decision Review System (DRS), to check Run-outs and in the Duckworth Lewis system.
  • Football – Technology is already impacting football; the goal-line technology and video-assisted replays provide a third-eye to the referee. AI-powered current and upcoming algorithms provide insights that will add value to the sport. Goal-Line Technology (GLT), Video Assistant Referee (VAR) are the technologies helping referees to give accurate results.
  • Baseball– AI collects all data of players such as speed, the angle at which they hit the shot. With the help of AI, it’s easier than ever to gather specific information on certain players, like the typical speed and angle at which they hit a baseball or the acceleration of the fielder that catches the ball and the way rapidly they’re ready to contribute the out. All of this information gives data analytics professionals the power to make insights for recruiters, giving them more information about prospects than ever before.
  • Tennis – IBM Watson AI technology is used in tennis to learn and interact with this sport. With the help of AI, the player creates strikingly realistic virtual tennis matches that supported real players. A team of researchers at Stanford University has created a man-made intelligence-based player called the Vid2Player that’s capable of generating startlingly realistic tennis matches—featuring real professional players.

And many other sports!!

AI Sports Companies:

  • Nex Team: It brings shooting-practice routines of NBA stars on its AI-driven training app. They use cutting edge mobile, AI, and computer vision technologies.
  • Catapult: They make wearable technology for players. They also provide athlete monitoring technologies across 1800 teams around the world.
  • Dojo Madness: Bayes Holding (formerly Dojo Madness) is a company of gaming and sports data, offering market-leading tools and services to business customers.
  • Mustard: It uses AI tools for player performance improvement.
  • Asensei: It’s a coaching platform that uses motion capture sensors in regular sports apparel to guide and correct an individual’s workouts.
  • Hawk-Eye: Vision-processing, video replay, and creative graphical technologies are provided by this company for sports.
  • Veo: Company that provides the solution to recording and watching sports without a cameraman.


Artificial Intelligence (AI) and Machine Learning (ML) technologies have been increasingly applied in the sports industry to improve performance, training, and fan engagement. Some examples include:

Performance Analysis: AI and ML can be used to analyze player and team performance, providing coaches and players with valuable insights into strengths and weaknesses. This can be used to improve tactics, identify areas for improvement, and track progress over time.

Injury Prevention: AI and ML can be used to analyze player movements and identify potential injury risks. This can be used to develop personalized training programs to reduce the risk of injury, and help players recover more quickly.

Player Scouting: AI and ML can be used to analyze large amounts of data on potential players, helping teams identify the best prospects and make more informed decisions during the draft process.

Sports Broadcasting: AI and ML can be used to enhance the sports broadcasting experience, providing real-time statistics, player tracking, and personalized recommendations to viewers.

Fan Engagement: AI and ML can be used to provide fans with personalized content and recommendations, helping to increase engagement and improve the overall fan experience.

Sports betting: AI and ML are also used in sports betting, to analyze data and make predictions on the outcomes of games, to help individuals make more informed decisions.

In the end, AI and ML technologies are being used widely in sports and it will surely shape the way sports are played, viewed, and marketed across the globe in the coming times!

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