When most people think of emerging technologies like computer vision and machine learning, they picture the innovations they can create for industries like healthcare, transportation and robotics. But computer vision can benefit every industry, and that includes athletics. This technology has the potential to change sports analytics, giving athletes more insight into their performance than ever before.

Here are a few unique use cases in the world of sports.

Real-Time Analytics

When watching a game, you must keep track of multiple players at once. You may have over 20 players to track on the field. That's a lot of eyes watching every movement. Things inevitably slip through the cracks, resulting in questionable calls.

Computer vision is a game-changer for sports analytics. Computer vision models can learn to track individual players and understand every play. It can process this data in seconds and provide accurate information in near real-time. That eliminates the need to have many eyes on the field, making it easier for announcers, referees and more. Imagine how much of a difference the technology could make in a sports club or arena. With a low risk of human error, it would reduce poor calls and help everyone stay in the loop.

High-Tech Training

Another way to utilize computer vision models is for training. Athletes go to great lengths to push their bodies to the limit. But being successful in sports is about more than brute strength or impressive agility. Athletes need to learn how to execute moves successfully.

Computer vision can analyze a player's movements and provide an in-depth analysis of their actions. Instead of using the result of the activity to gauge its success, you can get a play-by-play of every microsecond. See exactly where athletes went wrong and discover how they can improve.

This technology can be beneficial in any sport. But athletes requiring precise technique, such as martial arts or gymnastics, have the most to gain.

Sports Prediction

Computer vision can also play a part in predicting the outcome of individual plays and moves. While machine learning can't always predict the actions of free players, the technology can make accurate predictions of object trajectory.

Models can analyze everything from speed, pitch and player form to determine where a ball can go. Not only does that help predict outcomes, but it can also prove useful for continued athlete training.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at computer vision platform blog.