Date of Award

Spring 5-1-2026

Document Type

Honors Project

Degree Name

Bachelor of Science

Department

Mathematics

Department Chair or Program Director

Esunge, Julius

First Advisor

Hydorn, Debra

Second Advisor

Denhere, Melody

Major or Concentration

Applied Mathematics and Statistics

Abstract

In an era of baseball dominated by home runs and launch angles, the subtle art of baserunning is often overlooked, despite its measurable impact on winning games. Baserunning Runs (BsR) addresses this gap by quantifying the number of runs a player contributes through performance on the basepaths, capturing value beyond traditional metrics like stolen bases. This study constructs multiple regression models that predict BsR for Major League Baseball (MLB) players based on baserunning-related statistics. The primary objective is to examine the association between BsR and key predictors, including stolen bases (SB), extra bases taken (EB), and sprint speed (SS), while controlling for additional variables such as opportunities, years played, and physical attributes of the players. By evaluating linear, interaction, and Poisson regression models, this study identifies an appropriate modeling approach and highlights the underlying factors that contribute to effective baserunning in modern baseball.

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