How to Conclude a Suspended Sports League?

Ali Hassanzadeh (), Mojtaba Hosseini () and John G. Turner ()
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Ali Hassanzadeh: Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, United Kingdom
Mojtaba Hosseini: Tippie College of Business, University of Iowa, Iowa City, Iowa 52242
John G. Turner: Paul Merage School of Business, University of California, Irvine, California 92697

Manufacturing & Service Operations Management, 2024, vol. 26, issue 5, 1692-1711

Abstract: 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 nonrankings-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.

Keywords: COVID-19 pandemic; sports scheduling; rankings; concordance; predictive analytics; stochastic optimization; Frank–Wolfe algorithm; min-max regret; math programming; simulation; OM practice (search for similar items in EconPapers)
Date: 2024
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