An optimization of baseball fielder positioning using SEAM

In the last decade, professional baseball has witnessed significant statistical and analytical advancements that have profoundly impacted on-field strategies. This study delves into diverse strategies for positioning defensive players by employing deterministic and stochastic gradient methods. These methods optimize fielder alignments based on a synthetic distribution estimating batted ball distributions across all Major League Baseball (MLB) batter-pitcher matchups in any MLB ballpark. The primary aim is to devise a fielder placement model that minimizes expected batting average for balls in play by maximizing the density coverage of each fielder. This research explores various optimization techniques to identify the most effective approach in terms of both optimality and implementation practicality. This methodology could be generalized to determine optimal fielder alignments for similar sports or applications such as softball, cricket, or region design. The code for this work can be found in this GitHub repository.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Applied Mathematics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Alberts, Colin

Contributors dc:contributor

• Eck, Daniel J

• Gradient Descent
• Stochastic Gradient Descent
• Spatial Optimization
• Sabermetrics
• Big Data Applications And Visualizations

Statement dc:rights

• Copyright 2024 Colin Alberts

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124170

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124170