Date Presented

Spring 5-18-2024

Document Type

Thesis

First Advisor

Dr. Douglas Thomasey

Second Advisor

Dr. Thomas Ales

Third Advisor

Dr. Price Blair

Abstract

This study explores the creation of Offensive Wins Above Replacements (oWAR) models within the realm of Major League Baseball (MLB) based on individual player statistics for teams who participated in the 2022 postseason. The main objective was to construct a more refined oWAR model through the examination of the already existing oWAR model and its variables. The exploration was aimed at finding the impact that different variables relating to player performance have on the current oWAR and potentially reducing the number of variables used for the model. Through the use of statistical analysis techniques, such as regression analysis, it was found that not every variable included in the existing oWAR model was significantly influencing it. After running a regression analysis of the components of the oWAR equation, the results suggested that Base Running (BsR) was the component that significantly impacted the oWAR value the most. Base running component variables such as Stolen Bases (SB) and Caught Stealing (CS) were found to significantly impact the oWAR model. Other metrics such as Ground into Double Play (GDP), Hits (H), and Home Runs (HR) were also found to have some significant impact on the model. These findings indicate that there is potential for a more effective oWAR model that can be created using the base running variables and the significant traditional metrics.

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