Are players born earlier in the calendar year more likely to experience elite dropout?
Bibliographic record
Abstract
The relative age effect (RAE) has been consistently documented among elite football players at youth level but has been shown to dissipate at senior level. This research explores whether players born earlier in the calendar year, initially selected to play at an elite level, are more likely to be identified as dropouts at a later date. Statistical analysis is used to test for the presence and extent of RAE from a sample of almost 9,000 elite underage national league football players in the Republic of Ireland. Results reveal a bias towards players born early in the calendar year, and in the first quarter in particular. The bias is most pronounced at the youngest age group included in the analysis, at U15 level. Further statistical analysis assesses the differences between the observed distribution of births of 163 players who were identified as dropouts and the expected distribution of births. Players born earlier in the calendar year are also found to be more likely to be identified as dropouts from underage national league football in the Republic of Ireland. In comparison, their relatively younger counterparts, although less likely to be selected to play at an elite level initially, are significantly less likely to be identified as dropouts. Recommendations made based on the results include adopting a more strategic and long-term approach to be adopted during the initial player selection processes, and further education of coaches regarding youth development as well as the presence and consequences of RAE.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".