Revenue Sharing, Conjectures, and Scarce Talent in a Sports League Model
Why this work is in the frame
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Bibliographic record
Abstract
This article develops a model of a representative professional sports club operating in a league that has the option of adopting one of two different forms of revenue sharing: traditional revenue sharing or central-pool-type revenue sharing. To adopt either form of revenue sharing, the league requires tehat a majority of clubs increase their profit with adoption of the plan. We derive necessary conditions for either plan to garner enough support for a majority vote. The likelihood of forming a majority depends on the distribution of team revenues and the conjectures on acquiring talent that clubs possess. Competitive conjectures make the adoption of revenue sharing more likely, whereas cartel conjectures make its adoption less likely. This may partly explain why salary caps and revenue sharing tend to be used together in some leagues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it