Talent Identification and Relative Age Effects in English Male Rugby Union Pathways: From Entry to Expertise
Bibliographic record
Abstract
A common practice in youth rugby union is to group players based on (bi)annual age with fixed cut-off dates. The overrepresentation of players born at the start of the cut-off date and the underrepresentation of players born toward the end of the cut-off date are termed relative age effects (RAEs). The aim of this study was to examine RAEs during entry into professional and international rugby union pathways in England, as well as comparing them to their respective senior cohort: U15 Regional Academy Player (n = 1,114) vs. Senior Professional Player (n = 281) and U16–23 England Academy Player (n = 849) vs. Senior International Player (n = 48). Chi-square (χ2) analysis compared birth quarter (BQ) distributions against expected distributions. Odds ratios and 95% confidence intervals compared the likelihood of a BQ being selected. Findings revealed a significant overrepresentation of relatively older players compared with their relatively younger peers within both youth cohorts (P < 0.001; BQ1 = 42.5% vs. BQ4 = 9.6%; BQ1 = 36.5% vs. BQ4 = 15.2%). In comparison, there was no significant difference in the BQ distributions within both senior cohorts. Further, BQ4s were 3.86 and 3.9 times more likely to achieve senior professional and international levels than BQ1s and BQ2s, respectively. It is suggested that relatively younger players may have a greater likelihood of achieving expertise following entry into a rugby union talent pathway due to benefitting from more competitive play against relatively older counterparts during their development (e.g., reversal effects; the underdog hypothesis). Moreover, possible solutions (e.g., age and anthropometric banding; playing-up and playing-down) are discussed to encourage practitioners and policy makers to create the most appropriate learning environment for every player.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".