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Record W4403502294 · doi:10.1080/07420528.2024.2393880

Circadian variation in coaches’ decision-making in the National Football League’s evening games

2024· article· en· W4403502294 on OpenAlexaff
Vincent Bourgon, Félix Gabriel Duval, Geneviève Forest

Bibliographic record

VenueChronobiology International · 2024
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsEveningLeagueFootballCircadian rhythmVariation (astronomy)MorningChronotypePsychologyMedicineGeographyInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study was to explore whether National Football League (NFL) coaches show variation in their decision-making on fourth down when traveling through time zones. Data from visiting teams in games from 20 seasons (2000–2020) of the NFL were retrieved from online sources (n = 5360 games). Decision-making was measured with the percentage of offensive plays on fourth down. A factorial ANCOVA was done to verify whether travel direction had an impact on fourth downs in evening games, while controlling for the seasons. A moderation analysis was computed to verify whether the time of game moderates the relationship between longitudinal distance traveled and decisions on fourth downs. Results showed that in evening games, coaches in teams traveling westward called more offensive plays on fourth down, compared to when they traveled in any other direction. Results from the moderation analysis showed that only in evening games, further westward longitudinal degrees traveled predict more fourth downs. For the first time, this study offers insight that circadian misalignment may not only affect player performance but also influence coaching decisions in professional sports. These results beg the question whether other aspects of coaching or staff decisions show circadian variations in professional sports.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.070
GPT teacher head0.431
Teacher spread0.361 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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