Forget Big Data: Why Computer Vision is the Next Moneyball

With over 70 individual statistics tracked per game and up to 42 stats tracked per player, professional baseball is one of the most heavily analyzed sports in the world. 

Baseball statistics and big data have been used to predict everything from preventing injuries in pitchers to engineering championship teams. It was only a matter of time before the stats-obsessed sport and computer vision joined forces to create an unprecedented understanding of America’s favorite game. 

At the beginning of the 2020 MLB season, the organization announced a joint partnership with Google Cloud and Hawkeye Innovations to upgrade the league’s Statcast player statistic tracking system. Computer vision in the MLB is nothing new, mind you: The organization has been using Hawkeye’s SMART Replay system since 2015 to allow teams to challenge controversial calls in real time. However, the 2020 upgrade included the installation of over a dozen specialized cameras in all 30 of the MLB ballparks to enable full field vision for player and ball tracking. The resulting data streams are spectacular—and perhaps even cooler—available to the public online.

The Statcast system provides dozens of visual diagrams for coaches and fans to explore. From composites of the home run derby to individual pitches, users can study their favorite players or teams–offering unprecedented access to analyze, monitor, and geek out on America’s favorite pastime. 

In 2021, the MLB announced that it will begin using Statcast data to help crack down on the illegal doctoring of baseballs by players. Compliance officials will compare a pitched ball’s spin rate to career norms for individual pitchers, as well as monitor behavior before and after certain gametime events–something not possible without the integration of computer vision. There are even computer vision models that can predict the impact of a single player in a given game.

The ability to preserve and dissect the physics of a single pitch holds pretty amazing implications for computer vision use cases in other sports too. 

Imagine, for instance, that coaches with the help of computer vision, are able to optimize the foot placement of your tennis serve to maximize power or to make miniscule adjustments of your shoulder and hip alignment to help straighten out your golf swing. As the technology improves and mainstream understanding of computer vision and deep learning applications grow, mobile coaching apps that rely on phone cameras to record and analyze athletes could become commonplace.

That said, baseball isn’t the only All-Star computer vision use case–it might just be the most well known. Plainsight’s vision AI technology can be applied across industries to bring out insights hiding in plain sight. Schedule a demo today to learn more about how vision AI can provide valuable insights from operational analytics that transform your business processes.

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