Does Early Career Achievement Lead to Earlier Death? Assessment of the Precocity-Longevity Effect in Professional Basketball Players
Bibliographic record
Abstract
Objectives: To examine the precocity-longevity (P-L) effect in North American professional basketball players who debuted between 1946 and 1979, and to determine whether playing position and decade of play influenced the relationship between age of career achievements and lifespan. Methods: 1852 players were evaluated from a recognized sports archive (i.e., sports-reference.com), which provided information on date of birth, death, and career debut, playing position, and indicators of achievement (i.e., All-Star team and/or All-League team selection). Athletes were categorized as above or below the median age of professional debut, and median age of selection to first All-Star team and/or All-League team. Analyses of deceased players (n = 598) were comprised of bivariate correlations between age of achievement (age of debut, age of first All-Star game and age of first All-League team selection) and age of death, and t-tests to compared the average age of death of early and late achievers (p < .05). Survival analyses, using the entire sample (living and deceased players), compared the lifespans between those who debuted above and below the median age of achievement for each indicator of achievement. Results: Only the correlation between age of professional debut and age of death (r = .33, p < .001) and age of first all-star game and age of death (r = .29, p < .05) and the t-test comparing the average death age of early (66.4 y) and later (69.3 y) debut age groups (p =.01), reached statistical significance. However, survival analyses demonstrated a trend for lower risk of death for early achievers, with one exception (i.e., age of debut) this trend was not statistically significant. Conclusions: Results did not support the P-L hypothesis, suggesting that sample characteristics (i.e., physical fitness of high performance athletes), and measurement methodologies, may influence support for the proposed hypothesis in sport. However, future research would benefit form larger sample sizes and cause of death data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".