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Record W3128335637 · doi:10.33423/jabe.v22i4.2909

Broadcasting Revenues and Sporting Success in European Football: Evidence from the Big Five Leagues

2020· article· en· W3128335637 on OpenAlexvenueno aff
Tonny Stenheim, Andreas Gausemel Henriksen, Carl Magnus Stensager Stensager, Bjørn Ove Grønseth, Dag Øivind Madsen

Bibliographic record

VenueJournal of Applied Business and Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueRevenueFootballCompetition (biology)Broadcasting (networking)Distribution (mathematics)BusinessAdvertisingClubMarketingPolitical scienceFinanceComputer science

Abstract

fetched live from OpenAlex

This paper examines the association between broadcasting revenues and sporting success in Europe’s Big Five football leagues (England, Italy, Spain, Germany, and France), and in particular how the distribution and allocation of broadcasting revenues in Europe’s elite leagues are associated with the clubs’ domestic and international sporting success. The study makes use of a large hand-collected dataset comprising 8244 observations from 160 different clubs playing in one of these five leagues during the seven seasons from 2010 to 2017. The results indicate that the use of a uniform broadcasting revenue distribution model, which gives all clubs a relatively similar share of the pie, may increase domestic league competition, which in turn makes it tougher for one or two teams to dominate the rest. At the same time, there are some indications that a uniform broadcasting model is negatively associated with the clubs’ international sporting success. The use of a more top-heavy revenue distribution model, which leaves a smaller share for the worst-performing clubs, seems to enable the top clubs to retain both domestic and international sporting success.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.061
GPT teacher head0.208
Teacher spread0.147 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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