How AI could revolutionize sports: Coaches use algorithms to improve productivity, prevent injuries

Imagine a stadium where ultra-high-definition video broadcasts and drones carrying cameras track individual players’ bends during play, how high they jump or run fast – and use AI to accurately identify the risk of injury to athletes. in real time.

Coaches and elite athletes rely on new technologies that combine artificial intelligence with video to predict injuries before they occur, and provide customized training and training prescriptions to reduce the risk of injury. In the coming years, computer vision technologies similar to those used in airport face recognition systems will take this analysis to a new level, making portable sensors widely used by athletes today unnecessary, experts say. sports analytics.

This data revolution will mean that some overuse injuries could be significantly reduced in the future, said Stephen Smith, CEO and founder of Kitman Labs, a data company operating in several professional sports leagues with offices in Silicon Valley and Dublin. “There are athletes who treat their bodies as a business and have started using data and information to manage better,” he said. “We will see many more athletes play much longer and play at the highest level much longer.”

While offering perspectives on maintaining players’ health, this new frontier of AI and sport also raises difficult questions about who will have this valuable information – individual athletes or team managers and coaches who benefit from this data. Concerns about confidentiality are also emerging.

A baseball app called Mustard is among those already using computer vision. Videos recorded and sent by users are compared to a database of moves by professional pitchers, directing the app to offer prescribed exercises aimed at more efficient throwing. The Mustard, which is available in a free download, is designed to help ambitious players improve their performance and avoid repetitive movements that can cause long-term pain and injury, according to the CEO and co-founder. Rocky Collis.

The Mustard app records the mechanical movements of the baseball pitcher using computer vision technology.



Computer vision is also entering applications for other sports, such as golf, and promises to be important for both amateurs and professionals in the future. Algorithms using a form of AI known as machine learning, which extracts statistics from sensors and can analyze changes in body position or movement that could indicate fatigue, weakness, or potential injury, are now more widely used. The Liverpool Football Club in the United Kingdom said it had reduced the number of injuries to its players by a third from last season after adopting an AI-based data analysis program from Zone7. The information is used to adapt training instructions and to offer optimal rest time.

Football is one of the biggest users of artificial intelligence-based data analysis, as teams seek every kind of advantage in global sport. But some individual sports are also beginning to use these technologies. At the 2022 Beijing Winter Olympics, ten American skaters used a system called 4D Motion, developed by New Jersey-based 4D Motion Sports, to help track the fatigue that can result from making too many jumps. in fact, says Lindsay Slater, a sports science figure skating manager in the United States and an assistant professor of physical therapy at the University of Illinois at Chicago. The skaters attached a small device to their thighs and then reviewed the movement data with their coach when the workout was over.

“In fact, we’ve come to the point where we can really define the take-off and landing of a jump, and we can judge that the stresses in the thigh and trunk are quite high,” says Dr. Slater. “During the day, we found that athletes have reduced angular velocity, reduced jump height, they cheat more jumps, where these chronic injuries and overuse usually occur.” before expanding its use to more of its athletes.

The 4D Motion Sports golf training system automatically draws the swing plane as well as the path of the golf club using data from multiple sensors.


4D Motion Sports

Algorithms still have many obstacles to overcome when predicting the risk of injury. On the one hand, it is difficult to collect long-term data from athletes who jump from team to team every few years. In addition, the data collected by the sensors may vary slightly depending on the device manufacturer, while visual data has the advantage of being collected remotely without worrying that the sensor may be damaged, analysts say.

The psychological and emotional factors that affect performance cannot be easily measured: stress during contract talks, a fight with a spouse, poor food the night before. And the only way to really test the algorithms is to see if a player who has been identified as being at risk from an artificial intelligence program is actually injured in a game – a test that would violate ethical rules, said Devin Player, director of analysis at Toronto FC. one of the 28 teams in Major League Football.

“I think there may be a future in which these things can be trusted and reliable,” Mr Player said. “But I think there are significant sample size issues and ethical issues that we need to overcome before we can really reach that kind of threshold.”

Challenges also include data privacy issues and the question of whether individual athletes need to be compensated when teams gather information to power artificial intelligence algorithms.

According to Adam Solander, a Washington attorney who represents several major sports teams and data analysis companies, there are currently no regulations in the United States that prohibit companies from capturing and using player training data. He notes that the White House is developing recommendations on rules governing artificial intelligence and the use of personal data.

These regulations will have to strike a balance to allow potentially important technologies to help people, while respecting people’s privacy rights, Mr Solander said.


What is the future of artificial intelligence in sports? Join the conversation below.

For now, a sports data company that has adopted computer vision is using it not to predict injuries, but to predict the next superstar. The Paris-based SkillCorner collects television video from 45 football leagues around the world and manages it using an algorithm that tracks the location and speed of individual players, said Paul Nielson, the company’s general manager.

The company’s 65 customers are now using the data to spy on potential recruits, but Mr Nielson expects that in the near future, the company’s game video could be used in attempts to identify injuries before they occur. Still, he doubts that AI’s algorithm will ever replace a human trainer.

“During the game, you’re right there and you can smell it, feel it, almost touch it,” he says. “For these decision-makers, I think they are still less likely to actually listen to an insight that comes from an artificial intelligence source.

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