{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124170"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124170","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An optimization of baseball fielder positioning using SEAM","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Colin Alberts, accepted the attached license on 2024-04-26 at 15:01.","The student, Colin Alberts, submitted this Thesis for approval on 2024-04-26 at 15:07.","This Thesis was approved for publication on 2024-05-01 at 14:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20294 on 2024-09-16 at 00:33:39","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An optimization of baseball fielder positioning using SEAM"]}]}],"canonical_facts":{"dc:contributor":["Eck, Daniel J"],"dc:creator":["Alberts, Colin"],"dc:date":["2024-05","2024-05-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Colin Alberts, accepted the attached license on 2024-04-26 at 15:01.","The student, Colin Alberts, submitted this Thesis for approval on 2024-04-26 at 15:07.","This Thesis was approved for publication on 2024-05-01 at 14:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20294 on 2024-09-16 at 00:33:39","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124170"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Colin Alberts"],"dc:subject":["Gradient Descent","Stochastic Gradient Descent","Spatial Optimization","Sabermetrics","Big Data Applications And Visualizations"],"dc:title":["An optimization of baseball fielder positioning using SEAM"],"dc:type":["text"],"thesis:degree_discipline":["Applied Mathematics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}