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Competitive Balance

2012· book-chapter· en· W4247220847 on OpenAlex
Brad R. Humphreys, Nicholas M. Watanabe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLeagueBalance (ability)Sports economicsStrengths and weaknessesPromotion (chess)Competitive advantageEconomicsWork (physics)Political scienceEngineeringManagementPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract This chapter describes the uncertainty-of-outcome hypothesis and the seminal research of Simon Rottenberg, and then elaborates on the way that competitive balance is measured. Next, the exchange between Zimbalist and Fort and Maxcy about the nature of research on competitive balance and the effect of this exchange on subsequent literature are explored. Finally, the chapter surveys the research on competitive balance in promotion-and-relegation leagues, a common league arrangement outside of North America. Rottenberg's influence on sports economics is as great as that of any other economist to date, and knowledge of this seminal work is essential to understanding research in sports economics. Each of the many measures of competitive balance has relative strengths and weaknesses, and each captures a different element of competitive balance. It is difficult to determine whether one league has better competitive balance than another, because of the sensitivity of many competitive-balance measures to league composition and structure.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.177
Teacher spread0.143 · 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