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Record W2955945085

An exploratory examination of career precocity and mortality in professional basketball players

2012· article· en· W2955945085 on OpenAlexaff
Nick Wattie, Nima Dehghansai, Joseph Baker

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsYork UniversityOntario Tech University
Fundersnot available
KeywordsBasketballAthletesDemographyGerontologyPsychologyVariance (accounting)Regression analysisMiddle ageMedicineDevelopmental psychologyPhysical therapyHistorySociology
DOInot available

Abstract

fetched live from OpenAlex

Research on the correlates of talent development and expertise in areas outside of sport (e.g., politics and academia: McCann, 2001) suggests that those who reach notable career landmarks earlier in life also tend to have a shorter lifespan. To date, this phenomenon has yet to be explored in the domain of sport. This study examined mortality as a function of career precocity among deceased athletes who played in the National Basketball Association (NBA) in the 1940s and 1950s (N=377). Age of entry was positively correlated with age of death (r = .26, p < .001). Linear regression analysis also supported entry age as a significant negative predictor of lifespan (F (1,376) = 27.04, p < .001), accounting for approximately 7% of the variance in age of death. When entered into the regression equation entry ages of 22 and 25 predicted mean ages of death of 67.5 and 70.9, respectively. Interestingly, age of entry was negatively correlated with career length (measured as seasons played: r = - 28, p < .001). Importantly, these relationships may be confounded by athletes who were prematurely deceased and require further exploration. However, collectively, these results suggest a potentially negative consequence of early entry to professional basketball, which may be concerning considering the decline in mean entrance age throughout the history of the NBA. As such, if confirmed with more contemporary samples, these results may have implications for models of talent development in sport.

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)0.002
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 score0.003
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.247
Teacher spread0.215 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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