Date of Award

Spring 2023

Degree Type

Thesis

Department

Statistics

Director of Thesis

Dr. Joshua Tebbs

First Reader

Dr. Ting Fung Ma

Second Reader

Dr. Ting Fung Ma

Abstract

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.

First Page

1

Last Page

26

Rights

© 2023, Cade Stanley

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