From Fixtures to Fairness: Analytics-Driven Decision Making in Professional Sports

Abstract: Note the seminar meets at an unusual location, WMC 4335.

Title: From Fixtures to Fairness: Analytics-Driven Decision Making in Professional Sports

Problem definition: Professional sports leagues may be suspended because of various reasons, such as the recent coronavirus disease 2019 pandemic. A critical question that the league must address when reopening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Despite the rich literature on scheduling an entire season starting from a blank slate, concluding an existing season is quite different. Our approach attempts to achieve team rankings similar to those that would have resulted had the season been played out in full. Methodology/results: We propose a data-driven model that exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season composed of a subset of originally scheduled games. Our model introduces novel rankings-based objectives within a stochastic optimization model, whose parameters are first estimated using a predictive model. We introduce a deterministic equivalent reformulation along with a tailored Frank–Wolfe algorithm to efficiently solve our problem as well as a robust counterpart based on min-max regret. We present simulation-based numerical experiments from previous National Basketball Association seasons 2004–2019, and we show that our models are computationally efficient, outperform a greedy benchmark that approximates a non-rankings-based scheduling policy, and produce interpretable results. Managerial implications: Our data-driven decision-making framework may be used to produce a shortened season with 25%–50% fewer games while still producing an end-of-season ranking similar to that of the full season, had it been played.

Link to paper: pubsonline.informs.org/doi/abs/10.1287/msom.2022.0558

Part II: NBA Expansion: Opportunities to Reform the League

In this study, we explore how the NBA could restructure its divisions and conferences in light of potential league expansion. Building on optimization models, we consider two fairness-oriented formulations: a total travel distance minimization and a Nash bargaining framework that balances travel burden across teams, as well as distribution of media market size. Our approach evaluates realignment scenarios using geographic clustering and provides insights into how fairness and efficiency can be reconciled in league design. This work highlights the value of data-driven approaches in making strategic structural decisions for professional sports leagues.

Mathematics

Audience: researchers in the topic

PIMS-CORDS SFU Operations Research Seminar