Investigation of Relative Age Effect in Female Soccer: Born to Play?
Bibliographic record
Abstract
Early identification and development of “talented” athletes in youth sport is of primary interest to national governing bodies of sport and sport clubs across all sports. Selection bias during recruitment and planning the developmental pathways of athletes is a critical issue to address, and relative age effect (RAE) is one of the concepts to be investigated in this regard. The aim of this study was to examine the prevalence of RAE in U17 and U20 FIFA Women’s World Cup, and to investigate the role of age category, playing position and continents with regard to RAE. A total of 2016 female soccer players (U17=1008, U20=1008) participating in the last three consecutive U17 and U20 FIFA Women's World Cups were evaluated based on the birth month distributions. Inter-quartile differences were assessed using the Chi-square (χ²) goodness-of-fit test, and odds ratios (OR) and 95% confidence intervals were calculated to compare quartiles. RAE was more prevalent in U17 compared to U20 (χ2=43.865, p<.001, V=0.12; χ2=24.071, p<.001, V=0.09, respectively). For all positions, the number of female soccer players born in the first quarter of the year was higher than those born in the last quarter. In U17, RAE was statistically significant in all positions, while in U20 only defenders and midfielders’ distributions were significantly skewed. In conclusion, RAE is a critical issue to investigate in female soccer context, and age categories, playing position and continents seem such moderators of RAE that coaches and policy makers need to consider.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".