An exploratory examination of career precocity and mortality in professional basketball players
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".