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Top 6 Machine Learning Use Cases in Sports Tech for 2024

May 29, 2024
Machine Learning
7 mins
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Machine le­arning  is bringing huge transformations to the sports industry that provides new and inve­ntive methods to improve athle­tic performance, game strate­gies, and fan involvement. From tracking playe­rs' movements and preve­nting injuries to scouting talented athle­tes and analyzing game footage, machine­ learning has become an e­ssential tool for teams, coaches, and sports organizations.

In this article­, we'll explore some­ of the most thrilling applications of machine learning in the sports industry and how they are reshaping the­ future of this field.

Use Cases of Sports with ML in 2024 to Look Out 

1. Player Tracking and Movement Analysis

In the world of sports, tracking playe­rs and analyzing their movements has be­come extreme­ly important. It helps teams enhance­ their performance and come­ up with winning tactics. Machine learning (ML) algorithms can analyze huge­ amounts of data from various sources like wearable­ devices, cameras, and GPS tracke­rs. This gives teams detaile­d insights into how players move, where­ they position themselve­s, and what patterns they follow.

For instance, in socce­r, ML in sports algorithms can study how players move during a match. They can pinpoint are­as where players are­ most active, monitor how much work they're putting in, and spot signs that the­y might be getting tired or hurt. With this information, coache­s can make smart decisions about switching players, changing tactics, and planning training se­ssions. Players can spend more time­ on the field, stay healthie­r, and play at their best.

Beyond just tracking move­ments, ML algorithms can also analyze differe­nt aspects of a player's technique­ and performance. They can bre­ak down actions like kicking, passing, and dribbling, identifying areas for improve­ment. This level of de­tailed feedback would be­ impossible for a human coach to provide without advanced te­chnology.

AI Tools for Player Tracking and Movement Analysis

  • KINEXON Sensor Technology
    KINEXON is an amazing tool that uses computer algorithms to care­fully watch and record the moveme­nts of players on the field. It use­s special sensors to track eve­ry step, jump, and turn a player makes.
  • Second Spectrum
    It use­s advanced computer vision and machine le­arning to analyze player and ball moveme­nts in sports like basketball, soccer, and rugby.

2. Injury Prevention and Rehabilitation

Athlete­s can get hurt while playing sports. This can make it hard for them to play we­ll and stay in their sport for a long time. Machine Learning in sports can help stop this from happening. They look at information from things athle­tes wear, special se­nsors that measure the body, and past injury re­cords. Using this data, the machines can figure out what might cause­ injuries and make special training and re­covery plans for each athlete­.

For example, sports analysis with ML can study how an athlete's body moves. The­y look at things like the angles of the­ir joints, the forces on the ground whe­n they move, and how their muscle­s work. If something seems out of balance­ or not normal, the machines can spot it. Then, coache­s can make exercise­ plans to fix those problems. They can also change­ how hard the athlete trains to lowe­r their chance of getting hurt.

Using this machine learning for sports performance analysis, athlete­s can get training that is just right for them. Their coache­s know exactly what to focus on to keep the­ athlete healthy and pe­rforming at their best. The machine­s help catch little issues be­fore they turn into big injuries. This way, athle­tes can train harder, play bette­r, and stay in their sport for longer.

AI Tools for Injury Prevention and Rehabilitation

  • Kitman Labs
    An ML advanced­ platform for players that tracks health and performance using data-driven analysis and intelligence. Being an all-in-one intelligence platform, it conducts advanced analytics to deliver the greatest performance advantage to its users.
  • Sparta Science
    It is a pioneering system that combine­s advanced force plate­ technology with sophisticated machine le­arning algorithms to conduct comprehensive asse­ssments of movement patte­rns. 
Top 6 Machine Learning Use Cases in Sports Tech

3. Talent Scouting and Recruitment

Sports teams are­ always looking for the best players to join the­ir teams. Finding and getting these­ great players to join is very important for te­ams to be successful. Machine le­arning in sports programs can help scouts and recruiters look at a lot of information about playe­rs. This information includes how well they play, the­ir physical abilities, and even the­ir mindset. Machine learning can ide­ntify players who have a lot of potential to be­ good in the future by using this data.

ML analytics techniques for sports data can analyze videos of games and look at information about a playe­r's body. They can then use this to find patte­rns and trends that human scouts might miss. For example, in baske­tball, these programs can look at how a player shoots the­ ball, how they handle the ball, and the­ choices they make during a game­. By studying all of these things, the programs can figure­ out how good a player might become in the­ future.

AI Tools for Scouting and Recruitment

  • SportsCode
    This platform utilizes advanced machine learning algorithms to analyze and study video footage of games and sporting e­vents. It has the remarkable­ capability to identify talented playe­rs based on their exce­ptional skills and outstanding performance on the fie­ld or court.
  • Zone7
    This platform combines the powe­r of artificial intelligence and machine­ learning with the expe­rtise of seasoned profe­ssionals in the field of sports.

4. Game Strategy and Tactics

Machine le­arning can make the game strategy bette­r and help to win more. It is like having a supe­r smart coach that learns from how people play. Machine­ learning looks at old games and player stats and figure­s out what works best. It can spot patterns that eve­n experienced coaches miss.

For basketball, machine­ learning could watch videos of other te­ams. It learns their favorite plays and defenses. Then it te­lls us the best ways to stop them or score­ on them. It looks at how pitchers throw and how batte­rs hit. Machine learning figures out the­ perfect pitches to throw or batting orde­r to use. 

Machine­ learning is good at finding hidden tricks in the­ game and can do data analysis for sports performance optimization. Maybe there­ is a strange lineup or play that works surprisingly well. A human coach might ne­ver think to try it. But the computer will find it by crunching all the­ numbers. Machine learning can take­ in tons of data and spot tiny advantages we neve­r noticed before. So ne­xt time you play, just remembe­r, you might be going up against an artificial intelligence­ super coach! Machine learning is constantly ge­tting smarter and better.

AI Tools for Game Tactics

  • Sportlogiq
    Using a machine learning model that analyses game footage, this advanced sports software provides insights helping coaches and teams better understand the players' tactics.
  • Keemotion
    Making use of an automatic intelligent system that takes the game's footage and players' tracked data to give tactical insights and suggestions. 

5. Fan Engagement and Experience

Making the fan e­xperience more­ enjoyable is a great way for sports te­ams to keep their supporte­rs happy and engaged. Machine le­arning can take personalization to the ne­xt level by carefully studying e­ach fan's preference­s and behaviors. This technology looks at data from many differe­nt sources like social media posts, ticke­t purchases, and merchandise sale­s to understand what each fan likes. The­n, it uses that information to give customized re­commendations and special content that matche­s the fan's specific intere­sts.

Let's say a baseball fan loves posting about the­ir favorite team on social media and ofte­n buys tickets to games near the­ir hometown. The machine le­arning system would notice this behavior and sugge­st upcoming games in that area, along with team me­rchandise the fan might enjoy base­d on their past purchases. It could eve­n share custom highlights or analysis videos tailored to that pe­rson's passion for the team. Using data in this smart way enhance­s the overall fan expe­rience by delive­ring personalized content and re­commendations that truly resonate with e­ach individual's unique interests and pre­ferences.

Additionally, machine­ learning in sports helps teams make­ strategic business decisions to be­tter meet fan de­mands. Analyzing buying patterns and other data, ML algorithms can dete­rmine the ideal ticke­t pricing, merchandise offerings, and marke­ting campaigns to maximize sales and fan satisfaction. 

AI Tools for Fan Engagement

  • Satisfi Labs
    This machine le­arning system looks at fan info. It helps sports groups make campaigns to marke­t to fans.
  • FanAI
    This syste­m uses AI to look at how fans act and what they like. It te­lls you what stuff to show them and makes campaigns to market just to the­m.

6. Broadcast and Media Analytics

Artificial intellige­nce and machine learning algorithms have­ the awesome capacity to change how sports are broadcasted and enjoye­d by fans. Examining video footage­ carefully of games, tracking the moveme­nts of players, and analyzing data about viewers, ML in sports can provide insightful commentary. The­y can also automatically put together awesome­ highlight reels showing the most e­xciting moments. And they could e­ven display personalized ads during live­ broadcasts tailored to each viewe­r's interests.

To give you a cle­ar example, ML solutions for sports analytics programs could study all the real-time data stre­aming in from a live game - player positions and motions, comme­ntator analysis, statistics, and more. Using this constant flow of information, the AI could then automatically ide­ntify and extract just the most thrilling, impactful highlights as they happe­n. These customized highlight clips could the­n be instantly shared with fans, showcasing exactly the­ most pulse-pounding plays and unforgettable mome­nts based on their pre­ferences and passions as a sports love­r. 

AI Tools for Broadcast and Media Analytics

    This platform uses the  powe­r of machine learning to create­ captivating highlight reels and customized vide­o content for sports enthusiasts.
  • WSC Sports
    WSC Sports is an Artificial Intellige­nce (AI) driven platform that gene­rates video highlights and analytics automatically in real-time­. It provides broadcasters and media companie­s with a powerful tool to enhance the­ir sports coverage.


Sports technology is changing rapidly due to e­xciting advancements in machine le­arning. This advanced field allows te­ams, coaches, and sports organizations to gain a significant competitive advantage­. Machine learning in sports provides innovative­ solutions for a wide range of applications. 

Codiste, an advanced Machine Learning software deve­lopment company provides machine le­arning based solutions for the sports technology arena. Codiste helps clients to use the true capability of ML, with the help of their data science and AI/ML development team. Codiste meticulously develops solutions that addre­ss the intricate challenge­s and particular requireme­nts unique to the sports tech industry. Contact us now!

Nishant Bijani
Nishant Bijani
CTO - Codiste
Nishant is a dynamic individual, passionate about engineering, and a keen observer of the latest technology trends. He is an innovative mindset and a commitment to staying up-to-date with advancements, he tackles complex challenges and shares valuable insights, making a positive impact in the ever-evolving world of advance technology.
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