The absence of changes in the relative age effect present an opportunity for lower income soccer clubs to be more efficient than Europe’s elite
Bibliographic record
Abstract
An overrepresentation of athletes born earlier in the year compared with those born later in the year is known as the relative age effect (RAE). This is perceived to be due to physical selection bias which leads to higher degrees of exposure to coaching, physical training and competition at a younger age. Even with increasing knowledge and established interventions, clubs in Europe's top leagues still present a strong RAE. Scottish clubs have limited resources in comparison meaning academy efficiency is paramount. The main study aim was to assess changes in the RAE over a ten-year period in Scottish soccer. A secondary aim was to establish if physical differences exist across each quarter due to findings in English academy players that maturation status and not RAE is the main discrepancy for physicality. A retrospective analysis of 512 players from a Scottish academy over a ten year period was granted ethical approval. The impact of relative age effect was assessed against anthropometric and physical characteristics. The range of players in each quarter was Q1 37.0-42.9% versus Q2 22.8-32.4%, Q3 11.9-26.0% and Q4 7.1-14.3% with no impact of time on RAE profiles. Odds Ratio analysis indicate a greater chance of selection within the academy when assessing Q1vsQ4 players quarter comparisons (ranging 3.2-5.2 times more likely to be signed). When controlling for age group, multilevel modelling showed there were no significant differences across quarters in physical measures with the exception of a trivial CMJ difference. The lack of progression in the RAE profiles is disappointing however presents an opportunity for increased efficiency. By viewing the RAE as an under representation of Q4 players and using established corrective procedures, this can contribute to the unnecessary release of players from academies due to RAE, thus addressing challenges in financially restricted environments that resource rich environments such as Europe's elite have not yet overcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".