Simulation-Based Optimization of MLB Pitching Rotations for Postseason Success

Abstract

This thesis investigates how Major League Baseball (MLB) pitching rotations should be structured to maximize performance under the high-variance, short-series postseason format. Traditional roster construction methods focus on long-term regular season success, while existing postseason analyses treat games as independent events, ignoring sequencing effects. To address this gap, we develop a simulation-based framework that models outcomes at the game, series, and full playoff levels, and evaluate rotation strategies using Monte Carlo simulation. The resulting rotations are clustered into structural archetypes, revealing that top-heavy rotations achieve nearly equivalent performance at more cost-effective payrolls when compared to balanced rotations. Additionally, similar rotation costs across high-, medium-, and low-budget teams demonstrate diminishing returns to additional pitching investment. We then approximate championship probability with a surrogate model and optimize roster construction via integer programming, training the model on simulation outputs. The optimal solutions consistently feature young, surplus-value pitchers, highlighting the importance of internal development and timing in building postseason-optimized rotations.