Does Relative Age Affect Career Length in North American Professional Sports?
Bibliographic record
Abstract
BACKGROUND: Relative age effects (RAEs) typically favour older members within a cohort; however, research suggests that younger players may experience some long-term advantages, such as longer career length. The purposes of this study were to replicate previous findings on RAEs among National Hockey League (NHL) ice hockey players, National Basketball Association (NBA) basketball players and National Football League (NFL) football players and to investigate the influence of relative age on career length in all three sports. METHODS: = 1028) from 1980 to 1989. We investigated the possibility that younger players might be able to maximize their career length by operationalizing career length as players' number of games played throughout their careers. RESULTS: There was a clear RAE for the NHL, but effects were not significant for the NBA or NFL. Moreover, there was a significant difference in matches played between birth quartiles in the NHL favouring relatively younger players. There were no significant quartiles by career length effects in the NBA or NFL. CONCLUSIONS: The significant relationship between relative age and career length provides further support for relative age as an important constraint on expertise development in ice hockey but not basketball or football. Currently, the reason why relatively younger players have longer careers is not known. However, it may be worth exploring the influence of injury risk or the development of better playing skills.
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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) | 0.003 | 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".