  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.
