Whether it’s building a winning March Madness bracket or predicting who will win the Stanley Cup, sports analytics is an ever growing field within AI and machine learning. Moneyball was a major tipping point for the industry and the opportunities are continuing to grow. “Now, more than a decade and a half after the events of Moneyball the world of sports has evolved by leaps. It has increasingly incorporated different forms of technology, the most significant being the use of big data for sports analytics.” (Forbes)
In this AI Simplified video, Zachary Deane-Mayer explains how to use a model to predict the outcome of a sporting event. In order to predict the winner of a basketball game, Zach built a model using analyst ratings, tournament seeds, and the gambling odds for each game in the tournament. So, how would you use this model to predict the overall winner?
Watch this video to learn more from Zach:
Interested in more sports analytics? Check out these blog posts:
- Using Machine Learning to Peek Inside the Minds of NFL Coaches
- Taking Fantasy Football Analytics to the Next Level with Automated Machine Learning
- Using Automated Machine Learning to Predict NBA Player Performance