Estimating the Value of Brand Alliances in Professional Team Sports
Bibliographic record
Abstract
Brands often form alliances to enhance their brand equities. In this paper, we examine the alliances between professional athletes (athlete brands) and sports teams (team brands) in the National Basketball Association (NBA). Athletes and teams match to maximize the total added value created by the brand alliance. To understand this total value, we estimate a structural two-sided matching model using a maximum score method. Using data on the free-agency contracts signed in the NBA during the four-year period from 1994 to 1997, we find that both older players and players with higher performance are more likely to match with teams with more wins. However, controlling for performance, we find that brand alliances between high brand equity players (defined as receiving enough votes to be an all-star starter) and medium brand equity teams (defined by stadium and broadcast revenues) generate the highest value. This suggests that top brands are not necessarily best off matching with other top brands. We also provide suggestive evidence that the maximum salary policy implemented in 1998 influenced matches based on brand equity spillovers more than matches based on performance complementarities.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".