Optimizing Major League Baseball Divisional Realignment Under Expansion

Abstract

As professional sports leagues consider expansion, structural decisions such as divisional alignment become increasingly complex. This research examines how to optimally organize divisions in Major League Baseball under potential expansion scenarios. Using data-driven models that account for factors such as geographic distance, travel requirements, and team distribution, we evaluate alternative league structures. Our approach applies optimization techniques to generate and compare feasible realignment scenarios, aiming to improve travel efficiency while maintaining competitive balance. The model is informed by datasets on inter-team distances and market-related factors, enabling a more realistic representation of league dynamics. This work provides a framework for assessing expansion-driven realignment and highlights the role of operations research methods in supporting strategic decision-making in sports.