Relative age effects in professional German soccer: A historical analysis
Bibliographic record
Abstract
Relative age effects (RAEs) refer to the specific selection, participation and attainment (dis)advantages which occur as a result of physical and cognitive differences within annual age-grouped cohorts. The present study tracked the existence of RAEs in professional German soccer by examining RAEs in players, head coaches and referees who represented professional soccer clubs or officiated in the Bundesliga from 1963/64 to 2006/07. An additional objective was to consider the social-cultural mechanisms responsible for RAEs, so for a similar period, population and soccer participation information was also obtained. When players were categorised into half decade groups, chi-square analyses predominantly showed RAEs across the history of the Bundesliga, irrespective of dates used for annual age grouping in junior/youth soccer. RAEs were also apparent for head coaches but not for referees. Participation data indicated consistent and progressive growth from 1950 to 1990. RAEs influence the likelihood of attaining professional player and coaching status in German soccer. With many coaches being former players, inequalities associated with annual age-grouping appear to extend beyond a playing career. Officiating was not affected, with referees suggested to emerge from an alternative development pathway. Increased popularity of soccer may have propagated RAEs over time, through intensification of competition and selection mechanisms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".