He writes about using mathematics and computer science to design a baseball statistical model to examine how different strategies affect a game
Using 2008 Major League Baseball statistics, James Stoholski ’09 (Perkasie, Pa.) created a computer simulation which analyzes common baseball strategies and their usefulness for scoring runs. A double major in mathematics and computer science, Stoholski is working on his honors thesis with Jeff Liebner, visiting instructor of mathematics.
I spent the fall semester constructing a probability model simulating baseball so I can examine how different strategies affect a game. Basically, I went through every professional baseball game played last year, recorded the outcome of every at-bat, and placed them all into giant matrices.
Using these matrices, I can simulate professional baseball games and can implement different strategies and tactics that coaches often use, like stealing bases, bunting, etc. From there, I can examine if particular strategies work, and what instances are better than others.
For example, if there is a runner on first base and no one out, should the batter lay down a sacrifice bunt? Should the runner steal a base? Which batter in the lineup would be best for either of these strategies to be carried out? Simply put, I am objectively viewing how baseball is played and how a game can be won.
I’m really into sports statistics, primarily in baseball. My goal is to land a position with the front office for a professional team or work for a sports statistics/consulting firm. Baseball statistics are a popular topic at the moment, and I believe that if my work is successful, it will certainly aid me in my professional goals.