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SUMMARY:Ali Hassanzadeh (jointly hosted with Beedie's Technology and Opera
 tions Management Area) (University of Manchester)
DTSTART:20250815T170000Z
DTEND:20250815T190000Z
DTSTAMP:20260422T000603Z
UID:SFUOR/57
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/SFUOR/57/">F
 rom Fixtures to Fairness: Analytics-Driven Decision Making in Professional
  Sports</a>\nby Ali Hassanzadeh (jointly hosted with Beedie's Technology a
 nd Operations Management Area) (University of Manchester) as part of PIMS-
 CORDS SFU Operations Research Seminar\n\nLecture held in ASB 10908.\n\nAbs
 tract\nNote the seminar meets at an unusual location\, <B>WMC 4335</B>.\n\
 nTitle: <B>From Fixtures to Fairness: Analytics-Driven Decision Making in 
 Professional Sports</B>\n\nProblem definition: Professional sports leagues
  may be suspended because of various reasons\, such as the recent coronavi
 rus disease 2019 pandemic. A critical question that the league must addres
 s 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 l
 iterature on scheduling an entire season starting from a blank slate\, con
 cluding an existing season is quite different. Our approach attempts to ac
 hieve team rankings similar to those that would have resulted had the seas
 on been played out in full. Methodology/results: We propose a data-driven 
 model that exploits predictive and prescriptive analytics to produce a sch
 edule for the remainder of the season composed of a subset of originally s
 cheduled games. Our model introduces novel rankings-based objectives withi
 n a stochastic optimization model\, whose parameters are first estimated u
 sing a predictive model. We introduce a deterministic equivalent reformula
 tion along with a tailored Frank–Wolfe algorithm to efficiently solve ou
 r problem as well as a robust counterpart based on min-max regret. We pres
 ent simulation-based numerical experiments from previous National Basketba
 ll Association seasons 2004–2019\, and we show that our models are compu
 tationally efficient\, outperform a greedy benchmark that approximates a n
 on-rankings-based scheduling policy\, and produce interpretable results. M
 anagerial implications: Our data-driven decision-making framework may be u
 sed to produce a shortened season with 25%–50% fewer games while still p
 roducing an end-of-season ranking similar to that of the full season\, had
  it been played.\n\nLink to paper: https://pubsonline.informs.org/doi/abs/
 10.1287/msom.2022.0558\n\n \n\nPart II: <B>NBA Expansion: Opportunities to
  Reform the League</B>\n\nIn this study\, we explore how the NBA could res
 tructure its divisions and conferences in light of potential league expans
 ion. Building on optimization models\, we consider two fairness-oriented f
 ormulations: a total travel distance minimization and a Nash bargaining fr
 amework 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 efficien
 cy can be reconciled in league design. This work highlights the value of d
 ata-driven approaches in making strategic structural decisions for profess
 ional sports leagues.\n
LOCATION:https://researchseminars.org/talk/SFUOR/57/
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