Date of Award
Director of Thesis
Dr. Joshua Tebbs
Dr. Ting Fung Ma
In Major League Baseball (MLB), the outcome of a stolen base attempt has important implications. Success moves the runner closer to scoring, while failure records an out and removes the runner from the basepaths altogether. Therefore, it is important that the decision by a coach or player to steal a base is well-informed. In this thesis, I explore a statistical approach to making this decision. I train logistic regression and random forest models, using data about the game situation and about the runner, pitcher, and catcher involved in the stolen base attempt, to estimate the probability that a stolen base attempt succeeds. With an estimated probability of success, MLB teams can make better decisions on the basepaths.
Stanley, Cade, "Modeling the Probability of a Successful Stolen Base Attempt in Major League Baseball" (2023). Senior Theses. 602.