Beyond Suspension: A Two-phase Methodology for Concluding Sports Leagues
Ali Hassanzadeh, Mojtaba Hosseini, John G. Turner
TL;DR
The paper tackles the challenge of concluding a suspended sports season by selecting a subset of remaining games that yields end-of-season rankings close to the full-season outcome. It introduces a two-phase framework: predictive models estimate post-suspension game outcomes, and prescriptive models optimize the subset of games to minimize ranking dissimilarity with respect to the full season, using concordance and Euclidean-distance objectives. The authors develop a deterministic equivalent and a Frank-Wolfe–based algorithm (PW-FW), along with a robust min–max regret variant (PW-MMR), and demonstrate computational efficiency and high ranking fidelity on 14 NBA seasons, with extensions to manage strength-of-schedule. The work provides actionable, data-driven tools for policymakers to shorten seasons by 25–50% while preserving playoff eligibility and draft implications, offering practical implications for decision-makers facing disruptions like the COVID-19 pandemic.
Abstract
Problem definition: Professional sports leagues may be suspended due to various reasons such as the recent COVID-19 pandemic. A critical question the league must address when re-opening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Academic/practical relevance: 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 that which would have resulted had the season been played out in full. Methodology: We propose a data-driven model which exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season comprised 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. Results: We present simulation-based numerical experiments from previous National Basketball Association (NBA) seasons 2004--2019, and 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.