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Record W2104108898 · doi:10.1123/jsep.25.2.253

The “Hot Hand” Myth in Professional Basketball

2003· article· en· W2104108898 on OpenAlex

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

VenueJournal of Sport and Exercise Psychology · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsYork University
Fundersnot available
KeywordsCONTESTBasketballPsychologyDependency (UML)MythologySocial psychologyHistoryComputer sciencePolitical scienceArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

The “hot hand” describes the belief that the performance of an athlete, typically a basketball player, temporarily improves following a string of successes. Although some earlier research failed to detect a hot hand, these studies are often criticized for using inappropriate settings and measures. The present study was designed with these criticisms in mind. It offers new evidence in a unique setting, the NBA Long Distance Shootout contest, using various measures. Traditional sequential dependency runs analyses, individual level analyses, and an analysis of spontaneous outbursts by contest announcers about players who are “on fire” fail to reveal evidence of a hot hand. We conclude that declarations of hotness in basketball are best viewed as historical commentary rather than as prophecy about future performance.

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)0.001
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 score0.444
Threshold uncertainty score0.266

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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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