The influence of birth quartile, maturation, anthropometry and physical performances on player retention: Observations from an elite football academy
Bibliographic record
Abstract
Understanding the factors that influence player retention within elite youth football can be used to enhance current practices. This study investigated an English Category 1 academy to determine if birth quartile, somatic maturity, anthropometric and physical performance characteristics are associated with player retention across the developmental pathway. Birth dates of 355 elite players from Under 11 (U11) to U21 groups were categorised into birth quartiles, and logistic regression (odds ratio) analysis was used to determine the differences in retention. Multilevel modelling compared somatic maturity, anthropometry, countermovement jump, sprint time (10 and 30 m), agility T-test and Yo-Yo Intermittent Recovery Level 1 or 2 performance between retained and dropout players. Logistic regression (odds ratio) analysis revealed no significant differences between birth quartiles for the likelihood of being retained across age groups. Multilevel modelling revealed that retained players were typically older, advanced in maturity and superior in body size and physical performances compared to dropouts, with small to medium effect sizes typically observed. This study indicates that within a highly selective cohort of young football players, somatic maturity, anthropometric and physical performance characteristics, but not birth quartile, distinguish individuals that are subsequently retained or dropout in an age group-dependent manner. Youth football organisations should seek to implement multidisciplinary and dynamic talent selection and retention strategies to prevent inappropriate discrimination and loss of talented young players.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".