Jonah Lubin is our new instructor for Football Analytics. He is currently a Business intelligence intern with the Houston Texans. Recently named as a Semi-Finalist for the 2024 NFL Big Data Bowl. He majors in Sport Analytics at Rice University where he serves as the Student Director of Analytics and a scouting analyst for the football team.
This paper presents a novel simulation-based approach for optimizing batter lineups in minor league baseball. Our simulator goes beyond basic hitting statistics by incorporating key factors such as baserunner advancement, player speed, pitcher fatigue, handedness matchups, and situational outcomes like double plays and sacrifice flies. We propose a dynamic model that can simulate the expected runs scored by different lineups. We demonstrate the simulator’s usefulness by comparing real-world lineups, revealing optimal configurations that could significantly impact game outcomes. Our framework offers a valuable tool for coaches and analysts to make informed decisions in the complex and uncertain environment of minor league baseball.
This paper covers the optimal approach that the shortstop and second baseman should take when a runner from first base steals second base. It surrounds which infielder should cover second base, as well as what the positioning the other infielder should take. The determination of the optimal approach was ultimately decided by run expectancy saved and allowed.
