AI is on the verge of taking over sports like never before. Already, machine learning has been used by chess grandmasters to train their games and by stock, pickers to monitor market trends. With the help of AI, it won’t be long before they are on the court running drills (or “practicing” in any number of ways) with NBA stars or at-bat in MLB games. Here are some of the ways AI has impacted the world of sports.
1. Sports Analytics
Artificial intelligence has been used to bring the efficiency and analysis of the business world into the sporting world by separating actual sports performance from the number of hours played. These analytics, which can pinpoint which players are contributing to a team, can also be used to design scientific experiments–like in medicine–and test them on real athletes their coaches and managers guide. Another way machine learning is being used in sports is through deep learning.
2. Sport Nutrition
The fact that sports are an all-encompassing diet is changing. As AI is being used to analyze players’ performance, it’s also being used to scientifically study the effects of what they eat on the body. For example, AI is being used to predict the number of calories an athlete can consume without weight. AI also plays a role in how this nutrition is delivered. For example, AI is being used to study the different ways food affects blood glucose and how they can use an individual’s preferences to influence their choice of food.
3. Real-Time Virtual Pitching
In baseball, pitchers rely on the sport’s rulebook for guidance in making pitches. But when it comes to fastballs and off-speed pitches, batters have to adjust for speed and difficulty level. Now, AI is being used to figure out the best way to throw a pitch, depending on whether it’s hit low or high. Using deep learning, the computer can be trained to recognize patterns and make adjustments based on the pitch type and how it’s hit.
4. Sports Medicine
For many sports, the physical demands are intense enough to require professional medical attention for athletes who are serious about their chosen sport. For example, in rugby and baseball, when an athlete goes down with an injury, they receive treatment from medical professionals on-site. Machine learning is now being used to analyze the best way to produce and deliver treatment to the athlete. In this case, artificial intelligence was used to study the movements of cranial nerves. This information can then be used by doctors who, in conjunction with deep learning technology, can use past information about injuries and AI-produced data about what’s happening in the injury to determine new ways to treat players on site.
Dil Bole Oberoi
