The relative age effect in FIFA U-17 World Cup: The role of the playing position and the continent
Bibliographic record
Abstract
Background: The aim of this study was to examine the relative age effect of the countries which attended the 2017 FIFA U-17 World Cup and to research the relative age effect in terms of continents and the players’ position. Material and methods: 504 male football players from 24 countries which participated in the U-17 World Cup in India in 2017 were included in the study. Football players’ dates of birth were grouped into periods of three months in quarter years (Q): Q1 – January-March, Q2 – April-June, Q3 – July-September, Q4 – October-December. To study the sub-group differences of the relative age effect, meaningful chi-square (χ²) values were followed by calculating the odds ratio and %95 confidence intervals. To determine the effect size, Cramer’s V was used. Results: The relative age effect was based on quarter years’ distributions. Significant differences were found among age quartiles for all teams in FIFA U-17 World Cup. However, when the variables analysed were according to the continents, the relative age effect disappeared in Africa, Asia and Oceania. Conclusions: In the comparison of the players’ continents, a relative age effect was observed in Europe, North America, and South America. When the players’ positions are compared, a relative age effect was found in defenders, midfielders and forward players.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".