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Precocity-longevity Effects In Sport

2015· article· en· W2463460182 on OpenAlexaffabout
Srdjan Lemez, Nick Wattie, Chris I. Ardern, Joseph Baker

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLongevityBasketballDemographyLeagueProportional hazards modelGerontologyMedicinePsychologyInternal medicineHistory

Abstract

fetched live from OpenAlex

Investigations into the precocity-longevity hypothesis (PLH) have suggested that early high career achievement has a negative impact on the longevity of eminent persons (i.e., presidents, prime ministers, etc.) and professional baseball players. PURPOSE: Our purpose was to further examine the relationship between precocious achievement and lifespan differences in athlete populations; in particular, we considered different statistical approaches (e.g., survival vs. correlation) on this relationship. METHODS: The effects of debut age on longevity were examined in National Basketball Association (NBA) and Canadian-born National Hockey League (NHL) players. Both alive and deceased players were investigated using descriptive, correlation, regression, and survival analyses, also controlling for debut age, playing position, years played, and death age. RESULTS: Descriptively, median lifespan for early achievers was higher than it was for late-achievers in NBA players who debuted between 1947-1979 (N = 1847; 83.5 y vs. 81.5 y). In a subsample of deceased players from the 1940s and 1950s (n = 377), age of entry was positively correlated with age of death (r = 0.26, p < .001), and was a significant predictor of lifespan [F(1,376) = 27.04, p < .001]. Further, in Canadian-born NHL players who died between 1917-2010 (n = 937) debut age (M = 23.09) was correlated with death age (M = 68.27; r = .071, p < .001). Alternatively, the use of Kaplan-Meier and Cox regression survival analyses did not support the PLH in the NBA [1947-1979 debut; χ2 (1, N = 1847) = 2.53, p = .11], even after adjusting for playing position and decade of playing debut (Hazard Ratio: 1.06, 95% Confidence Interval: 0.86-1.32), nor in the ice-hockey sample [1917-1986 debut; χ2 (1, N = 2971) = 2.35, p = 0.12], after adjusting for playing position and years played (HR: 0.91, 95% CI: 0.79-1.05). CONCLUSION: These data suggest that the type of statistical analysis influences support for the PLH. Future analytical strategies must be exceedingly cautious in circumventing potential biases when testing this hypothesis in both athlete (e.g., National Football League) and eminent samples (e.g., Nobel Prize recipients).

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)0.005
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 score0.023
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.259
Teacher spread0.230 · 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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