Selection and re-selection throughout a national talent pathway: Exploring longitudinal relative age effects in Northern Ireland male soccer
Bibliographic record
Abstract
The purpose of this study was to investigate the longitudinal prevalence of relative age effects (RAEs) across playing levels and positions in Northern Ireland international male soccer. Birthdates of U17 (n = 276), U19 (n = 320), U21 (n = 331), and senior (n = 108) international players between 2011 and 2023 were recorded. Chi-square tests and odds ratios with 95% confidence intervals were used to compare observed and expected birthdate distributions. A selection bias toward relatively older players was evident at U17 and U19 playing levels as well as defensive and midfield positions. In contrast, more relatively younger senior and forward players were selected. Longitudinal analyses revealed that of those players who were initially selected at U17, more relatively older players were re-selected at subsequent playing levels, but there were no birthdate asymmetries amongst new players added after U17. The results of this study demonstrate RAEs are prevalent across Northern Ireland international male soccer and are influenced by playing level as well as position. These findings have important implications for policy makers and practitioners as relatively older players are overrepresented at youth but not senior level, which questions the efficacy of this (un)conscious bias in the talent pathway.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".