Date of Graduation
12-2025
Document Type
Thesis
Degree Name
Master of Arts in Economics (MA)
Degree Level
Graduate
Department
Economics
Advisor/Mentor
McGee, Peter
Committee Member
Jung, Hyunseok
Second Committee Member
Butts, Kyle
Keywords
contract design; fixed effects regression; MLB incentives; performance contingent bonuses; principal–agent; random forest
Abstract
This thesis examines how guaranteed contract length influences player effort and market valuation in Major League Baseball using high resolution Statcast data across over 1,200 player season observations. Fixed effects regressions on the 2015 to 2024 seasons show directionally consistent but statistically imprecise shirking and final year effort spikes in hard hit rate and sprint speed. Machine learning models (OLS and Random Forest) predict next year Dollars Over League Average (DOL) with out of sample MAE approximately $11.4–11.8 million and approximately 0.23–0.27, with plate appearances, hard hit rate, sprint speed, and contract timing as top predictors. Embedding these findings in a principal–agent framework with concave utility and convex effort costs reveals that per unit bonuses on low elasticity metrics would be prohibitively expensive. Instead, I propose threshold and menu style incentive schemes anchored at empirically identified performance inflection points, combining theoretical rigor, interpretable forecasts, and practical contract design to guide front offices in aligning compensation with effort.
Citation
Jordan, C. (2025). A Thesis on Incentive Structures, Shirking Dynamics, and Market Valuation in Major League Baseball: A Statcast Analysis of Worker Effort Levels. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5992
