“Birthday-Banding” as a Strategy to Moderate the Relative Age Effect: A Case Study Into the England Squash Talent Pathway
Bibliographic record
Abstract
The relative age effect (RAE) is almost pervasive throughout youth sport, whereby relatively older athletes are consistently overrepresented compared to their relatively younger peers. Whilst researchers regularly cite the need for sport programmes to incorporate strategies to moderate the RAE, organisational structures often continue to adopt a one-dimensional (bi)annual-age group approach. In an effort to combat this issue, England Squash implemented a ‘birthday-banding’ strategy to their Talent Pathway, whereby young athletes move up to their next age group on their birthday, with the aim to remove particular selection time-points and fixed chronological bandings. Thus, the purpose of this study was to examine the potential effects of the birthday-banding strategy on birth quarter (BQ) distributions throughout the England Squash Talent Pathway. Three mixed-gender groups were populated and analysed: (a) ASPIRE athletes (n = 250); (b) Development and Potential athletes (n = 52); and, (c) Senior Team and Academy athletes (n = 26). Chi-square analysis and odds ratios were used to test BQ distributions against national norms and between quartiles, respectively. Results revealed no significant difference between BQ distributions within all three groups (P >.05). In contrast to most studies examining the RAE within athlete development settings, there appeared to be no RAE throughout the England Squash Talent Pathway. These findings suggest that the birthday-banding strategy may be a useful tool to moderate the RAE in youth sport.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".