arXiv subjects

Ali Hassanzadeh

Publications and source records attributed to Ali Hassanzadeh.

3 recordsLinked to original sources

arXiv2025

Fairness, Travel, and Market Potential: An Optimization Framework for NBA Expansion

The National Basketball Association (NBA) is actively considering the addition of two expansion teams, raising the question of how to restructure its conferences and divisions to balance travel efficiency, fairness, and revenue opportunities. This study fills a gap at the intersection of sports operations and strategic league design by providing a quantitative framework for expansion planning. We develop two optimization models: one minimizing total travel distance and another using a Nash Bargaining framework to balance travel burdens while accounting for media market size. Using data from all 30 current franchises and six candidate cities (Seattle, Las Vegas, Montreal, Vancouver, Tampa, and Mexico City), we evaluate 15 pairwise expansion scenarios under alternative season lengths and divisional formats. Results show that while the distance-minimizing model produces geographically tight divisions, the Nash Bargaining model generates more balanced outcomes, particularly for geographically isolated franchises, with only modest efficiency losses. Our study offers a flexible decision support framework for league executives, policymakers, and sports economists. It provides evidence-based insights into how expansion decisions can balance operational efficiency, fairness in competition, and access to major media markets in a multi-billion-dollar sports league.

physics.soc-ph↗

arXiv2024

Beyond Suspension: A Two-phase Methodology for Concluding Sports Leagues

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.

math.OC↗

arXiv2019

A Domain-Theoretic Bishop-Phelps theorem

In this paper, the notion of $c$-support points of a set in a semitopological cone is introduced. It is shown that any nonempty convex Scott closed bounded set has a $c$-support point in a cancellative $bd$-cone under certain condition. We also introduce the notion of $wd$-cone and then we prove a Bishop-Phelps type theorem for $wd$-cones, especially for normed cones, under appropriate conditions. Finally, using of the Bishop-Phelps technique, we obtain a result about the fixed points of a mapping on $s$-cones.

math.FA↗