An Analysis of the Key Components of WAR in Baseball
Location
Room 232, Schewel Hall
Access Type
Campus Access Only
Start Date
4-17-2024 10:00 AM
End Date
4-17-2024 10:15 AM
College
Lynchburg College of Arts and Sciences
Department
Mathematics
Keywords
Wins Above Replacement (WAR), Offensive WAR (oWAR), Multiple Linear Regression, Offensive Metrics, Player Evaluation, Baseball Analytics, Model Evaluation, Player Value, Contextual Adjustments, Quantitative Analysis.
Abstract
In this project we will be exploring Wins Above Replacement (WAR) as an important metric of player performance evaluation in baseball and also as a means for combining various player contributions into a single numerical value. This project is inspired by the limitations and discrepancies within existing WAR models. The project explores past research on WAR in order to develop a new model utilizing the multiple linear regression technique in order to assess its efficiency compared to the current models. The focus of this project primarily pertains to the offensive aspect of war, commonly known as oWAR. This project explores the existing WAR structures and identifies key components and methods used for their establishment. The new WAR model is constructed based on analysis and pre-established methodologies developed by Baseball-Reference and FanGraphs WAR models. By applying advanced statistical analysis techniques a WAR model is created, integrating multiple variables relating to offensive statistics and also accounting for contextual adjustments for various game conditions. The methodology includes a detailed data collection and analysis as well as model construction, while utilizing historical data regarding player statistics and performance for the 2022 Major League Baseball (MLB) season. The regression analysis will help determine the quality of the proposed model that aims to assign a numerical value to player contribution and determine if there is a more accurate and comprehensive way of determining WAR, while taking into account the limitations and biases. Statistical tests will be used to determine the model’s accuracy and utility in determining player value. The implications of this research aim to extend the knowledge of baseball analytics and offer insights regarding the methodologies of player performance metrics and their applications.
Faculty Mentor(s)
Dr. Douglas Thomasey Dr. Thomas Ales Dr. Price Blair
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An Analysis of the Key Components of WAR in Baseball
Room 232, Schewel Hall
In this project we will be exploring Wins Above Replacement (WAR) as an important metric of player performance evaluation in baseball and also as a means for combining various player contributions into a single numerical value. This project is inspired by the limitations and discrepancies within existing WAR models. The project explores past research on WAR in order to develop a new model utilizing the multiple linear regression technique in order to assess its efficiency compared to the current models. The focus of this project primarily pertains to the offensive aspect of war, commonly known as oWAR. This project explores the existing WAR structures and identifies key components and methods used for their establishment. The new WAR model is constructed based on analysis and pre-established methodologies developed by Baseball-Reference and FanGraphs WAR models. By applying advanced statistical analysis techniques a WAR model is created, integrating multiple variables relating to offensive statistics and also accounting for contextual adjustments for various game conditions. The methodology includes a detailed data collection and analysis as well as model construction, while utilizing historical data regarding player statistics and performance for the 2022 Major League Baseball (MLB) season. The regression analysis will help determine the quality of the proposed model that aims to assign a numerical value to player contribution and determine if there is a more accurate and comprehensive way of determining WAR, while taking into account the limitations and biases. Statistical tests will be used to determine the model’s accuracy and utility in determining player value. The implications of this research aim to extend the knowledge of baseball analytics and offer insights regarding the methodologies of player performance metrics and their applications.