Prevalence of Relative Age Effect in Russian Soccer: The Role of Chronological Age and Performance
Bibliographic record
Abstract
The relative age effect (RAE) has been well studied in adolescent and adult soccer players; however, less information has been available about children engaged in regular soccer training and the role of performance. Thus, the aim of the present study was to examine the prevalence of RAE in children and adolescent soccer players, as well as the role of age and performance. Russian soccer players (n = 10,446) of various ages, playing positions and performance levels were examined for their date of birth. It was observed that RAE was widespread in Russian soccer teams of all age groups. RAE was most pronounced in children teams of the top tier Russian soccer academies and junior Russia national teams, where the proportions of soccer players born in the first quarter were 43.9% and 39.8%, respectively, whereas those born in the fourth quarter of the year were 7.7% and 6.3%, respectively. In top tier soccer academies, RAE did not vary by age group. In the middle tier soccer academies, RAE was less pronounced. It was still prevalent in the junior teams of the top tier clubs of the Russian Premier League, where 14.3% of the soccer players were born in the fourth quarter of the year compared to 42.9% born in the first quarter of the year. RAE can be observed in the top tier Russian adult teams as well, although it is less pronounced there. In summary, RAE is highly prevalent in Russian children and junior soccer and is associated with the level of competitiveness. At the same time, the proportion of players born in the fourth quarter of the year is higher in adult teams than in junior and youth teams, which is most likely due to the wider selection of players, not limited by their age and place of residence. In junior teams, RAE results in a bias towards selection of players who are more physically mature, whereas children who may be more talented but are less developed due to their younger chronological age tend to be overlooked.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".