If you have read the book Moneyball, or seen the movie, you will be familiar with Sabermetrics. That’s the data science behind picking great baseball prospects. The goal is finding great baseball players who may play for poor teams, and assessing the true value of a player’s contribution to a team. It’s not as easy as it sounds.
You might think you want the player with the greatest number of home runs, but what if he strikes out 9 out of every 10 times at bat? What if he tends to leave runners on second- and third-base a lot. Or do you get the player with the great hit percentage (.290!) but can’t run as fast as other players and often gets called out in double-plays.
Sabermetrics is the data science of analyzing all of the numbers and statistics of baseball, and try to come up with a true measure of a player’s (or team’s) worth. Hitting, running, fielding.
From the edX course page, Sabermetrics 101: Introduction to Baseball Analytics:
This course will cover the theory and the fundamentals of the emerging science of Sabermetrics. We will discuss the game of baseball, not through consensus or a fan’s conventional wisdom, but by searching for objective knowledge in hitting, pitching, and fielding performance. These and other areas of sabermetrics will be analyzed and better understood with current and historical baseball data.
I bet it involves a lot of deep math. But if you’re into sports, AND into math, should be a fun course.