Please note that this online seminar is a members-only event for OPTIMA researchers, academics, students and industry partners.

Title: How to conclude a suspended sports league?

Abstract: 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. 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. 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 apredictive model. We propose sample average approximation and Frank-Wolfe algorithms for solving these problems efficiently. We present simulation-based numerical experiments from previous National Basketball Association (NBA) seasons 2004-2019, and show that our models are computationally efficient. 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.

Bio:Ali joined the Alliance Manchester Business School as an assistant professor in management science in September 2021, after completing his PhD in operations and decision technologies at the Paul Merage School of Business, University of California Irvine. He holds an MSc from KOC University, Istanbul (2015) and a BSc from Sharif University of Technology, Tehran (2013), both in industrial engineering. He has worked briefly at Bayer Crop Science, Saint Louis, Missouri, as a datascience intern in 2019. Ali’s research has mostly centered around developing mathematical models to help high-level decision makers to improve operations in their organizations. Theory-wise, he primarily has worked with combinatorial optimization models, distributed optimization, algorithm design, and machine learning techniques. Application-wise, he has been involved in projects in sports analytics and resource sharing and resource exchange in multi-agent systems (e.g., alliance of production and service companies). He has extensive teaching experience in the business school for various programmes, including MBA, Master of Business Analytics, and undergraduate, and different courses including predictive analytics, analytical decision making, management science, operations management, and statistics. Ali was a fellow of the Division of Teaching Excellence and Innovation (DTEI) at UC Irvine in 2020 and holds a certificate in “Remote Instruction” from that division.

WED 8 FEBRUARY 4:00 PM – 5:00 PM AEDT MELBOURNE
Zoom link provided in email to members.