Graduation Year

2025

Date of Submission

12-2024

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Department

Economics

Reader 1

Brian Bullard

Rights Information

Paul F Wendler

Abstract

This paper introduces a new baseball statistic, Weighted WAR (wWAR), designed to enhance Major League Baseball (MLB) roster optimization by incorporating team-specific factors into player valuation. Traditional WAR (Wins Above Replacement) offers a generalized measure of player performance relative to the league minimum salaried player. The study bridges the gap by developing wWAR, a customizable, team-specific framework for evaluating player impact. Taking player data, team data, and salary data from 2021-2023 the study proves the significance of wWAR’s impact relative to the traditional WAR statistic using case studies and regression analysis. The results demonstrate that wWAR outperforms traditional WAR in forecasting team regular season success, particularly for small and large-market teams where payroll efficiency and strategic resource allocation are critical. The research does not undermine the value of WAR, but rather provides an alternative way to look at a player's WAR based on team specific constraints. While limitations in data availability, data manipulation, and non-quantifiable factors remain, wWAR provides a transformative tool for enhancing team performance and competitive balance in the MLB

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