Fairness, Travel, and Market Potential: An Optimization Framework for NBA Expansion
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".