Jun Kang | 2026 I.S. Symposium
狈补尘别:听Jun Kang
Title: Optimizing MLB Batting Orders Using a Context-Dependent Monte Carlo Simulation and Metropolis-Hastings Algorithm
Major: Statistical & Data Sciences
惭颈苍辞谤:听Economics
础诲惫颈蝉辞谤:听Cynthia Lester
This study develops a simulation-based framework to optimize Major League Baseball batting orders using a non-homogeneous Markov-chain model of inning progression. Using 2025 MLB regular-season batting event data from FanGraphs, plate-appearance outcome probabilities are estimated conditional on base-out context and opposing pitcher handedness. These probabilities define the transition mechanism for a nine-inning run-scoring simulator. For each team, a fixed set of nine hitters is selected and the optimal batting order is searched over the $9!$ permutation space using a Metropolis-Hastings procedure, a shallow exploration phase to identify strong candidate orders and a deep Monte Carlo re-evaluation phase to reduce simulation noise.
Across the league, the optimized batting orders vary with pitcher handedness, indicating that optimal lineup structure is conditional. At the team level, simulation-based run estimates are broadly consistent with observed offensive strength. Overall, the results suggest that optimal batting-order design is conditional on matchup context and that non-homogeneous Markov modeling coupled with stochastic search can generate interpretable lineup recommendations under realistic data and computational constraints.
Posted in Symposium 2026 on May 1, 2026.
