Lingering effects of relative age in basketball players' post athletic career
Bibliographic record
Abstract
Relative age effects (RAEs) are known to affect likelihood of attaining sporting excellence (Baker, Schorer, & Cobley, 2010). However, research examining the enduring long-term consequences is limited. Cobley, Schorer, and Baker (2008) observed RAEs for soccer coaches, but not for soccer referees. This study examined the RAE among male players in the first German basketball league and determined the longevity of this effect to post athletic careers in basketball. Birth-dates of 142 players, 26 coaches, 32 referees, and 16 commissioners were obtained from the official First German basketball league for the 2009/10 season. Dates were categorized into quartiles according to the calendar date used for annual age-grouping for international basketball. As expected significant RAEs were revealed for players, ?²(3) = 12.99, p < .01, ? = .30. Furthermore, there were descriptive trends observed for the other groups; however, these differences were not statistically significant due to the small samples. Therefore, all post-athletic career groups were combined, which resulted in a significant RAE, ?²(3) = 10.22, p = .02, ? = .37. These results suggest that there could be persistent long-term consequences to the RAEs thought to originate during the early stages of a playing career. As others have indicated (Musch & Grondin, 2001), RAEs are a persistent inequality in high performance sport; one that, unfortunately, is not easily remedied.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".