“More players can reach national and international levels”: coaches perceptions of “birthday-banding” in youth squash and its potential for minimising relative age effects
Bibliographic record
Abstract
Relative age effects (RAEs) are common across many youth sports that use age group structures to band athletes. This creates a significant overrepresentation of those who are born near the start of the selection cut-off date across talent pathways compared to those born towards the end. In an attempt to identify, select, and develop the most talented squash players based upon their long-term potential, England Squash designed and implemented the "birthday-banding" approach (i.e., athletes compete with and against those of the same age and move up to their next birthdate group on their birthday), which has indicated promising results for moderating RAEs across their player pathway. However, little work has focused on the perceptions of interest-holders on this approach. For this reason, the purpose of this study was to use semi-structured interviews with fifteen England Squash Talent Pathway coaches, to better understand the mechanisms of the birthday-banding approach as well as its potential benefits and limitations. Using thematic analysis, three higher-order themes were found that comprised of six lower-order themes: (a) considering organisational structures (e.g., understanding selection processes, and reflecting on competition structures and performance outcomes), (b) building appropriate settings (e.g., promoting flexibility and fluidity in groups, and creating an environment that fosters long-term development), and (c) facilitating individual athlete development (e.g., encouraging holistic development and progression, and evaluating physical and skill development). Overall, coaches spoke highly of the implementation of birthday-banding, noting the value in creating fairness for athletes who might have been removed due to their birthday and maturation levels. Coaches also reported appreciating seeing athletes having varying competition within and across a year, as sometimes athletes would be relatively older and younger than their peers within the same 12 months. Some considerations and concerns were also raised about implementing a birthday-banding approach, which have been highlighted to inform continued improvements for both athletes and coaches in the system.
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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 | 0.004 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".