MétaCan
Menu
Back to cohort
Record W2096919727

Free Agent Auctions and Revenue Sharing: A Simple Approach

2007· preprint· en· W2096919727 on OpenAlexaff
Duane W. Rockerbie

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsRevenue sharingRevenueCommon value auctionBiddingRevenue modelRanking (information retrieval)BusinessRevenue assuranceMicroeconomicsAffect (linguistics)SalaryMarginal revenueEconomicsComputer scienceFinanceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This paper uses a simple approach to address the issue of how revenue sharing in professional sports leagues can affect the allocation of free agent players to teams. To affect the allocation of free agents, the imposition of revenue sharing must alter the ranking of bidding teams in terms of maximum salary offers. Two types of revenue sharing systems are considered: traditional gate revenue sharing and pooled revenue sharing. The paper suggests that team rankings for ability to pay are not affected by pooled revenue sharing, however the distribution of player salaries will be compressed. Traditional gate revenue sharing can alter the ability to pay rankings for teams, depending upon playing schedules and the closeness of revenues between closely ranked teams.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0060.009
Open science0.0040.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.088
GPT teacher head0.310
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicSports Analytics and PerformanceFrench-language works237,207