A data-driven optimization approach to baseball roster management

Sean Barnes (), Margrét Bjarnadóttir (), Daniel Smolyak () and Aurélie Thiele ()
Additional contact information
Sean Barnes: Netflix
Margrét Bjarnadóttir: University of Maryland College Park
Daniel Smolyak: University of Maryland College Park
Aurélie Thiele: Southern Methodist University

Annals of Operations Research, 2024, vol. 335, issue 1, No 2, 33-58

Abstract: Abstract Each year, major league baseball (MLB) teams face complex decisions about which players to retain and which players to recruit. In addition to operational, team and budget constraints, these decisions are further complicated by the fact that an athlete’s future performance and its impact on the team are both uncertain. In this paper, we combine prediction modeling with decision optimization to study the MLB free agent market. We develop optimization models for the allocation of a team’s recruitment budget using six different metrics that evaluate a player’s contributions to a team’s success. We consider both an ideal case, where each team can choose among all free agents, and a sequential case, where we assume that teams with stronger appeal (big market) are more successful in attracting talent, while teams with less pull must optimize their rosters over a much smaller pool of remaining players. Using the best-performing metric, which takes into account both players’ positions and their positional flexibility, we develop a series of quantitative tools that help teams, especially those with small budgets, identify (1) the players who deliver a key competitive advantage to their teams, appearing in both their ideal and sequential rosters and (2) the players who are in many ideal rosters and thus are likely to be hired by teams with big budgets, perhaps at a substantial salary premium. In order to gain and maintain an edge in the fiercely competitive free agent market, teams need to continuously adapt their strategies, and our models represent a first step towards prescriptive (not just predictive) analytics designed to help them do so. Further, our analysis indicates that a few players are in high demand from many teams (for instance, in every year of the period considered, the ten most in-demand players appear in the ideal rosters of at least seven teams), while most players appear in one ideal roster or none at all. Our models go beyond players’ individual performance metrics to help teams understand which players will be in high demand due to teams’ position needs in a given year. The results further emphasize the increasing importance of contract extensions as a strategy to bypass the free agent market.

Date: 2024
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DOI: 10.1007/s10479-023-05725-4

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