Efeito da idade relativa no futebol : análise em jogadores do torneio sul-americano sub 17.
Bibliographic record
Abstract
The presented study explore the effect of relative age (RAE) in soccer players of the Youth South American Tournament Under 17 2015. A quarter of birth of 221 players was identified corresponding to 10 countries which have participated in this Tournament in 2015, from the website http://www.conmebol.com/pt-br/sub17/sul-americano-sub-17-paraguai/planteis. The category was the birth's month of each athlete classified in quarters (1st Quarter: January-March; 2nd Quarter April-June; 3rd Quarter: July-September, 4th Quarter: October-December). For data analysis, Chi-square test was used with significance level of 5%. It was notice that the predominance of players born on the 1st quarter was higher compared to other birth's quarters (X2 = 61.081, df = 3, p <0.001). The division of birth dates of the players in quarters of the year prove a greater representation of those who has born in the first quarter of the year (47%). The athletes had higher representation in January, February and March compared to the 2nd quarter (22%), 3rd quarter (19%) and 4th quarter (13%) of the year. The RAE was observed in players who participated in the Youth South American Tournament Sub17 2015, suggesting the existence of the relative age effect. It was noticed that soccer players born in the First and Second quarter of the year are the majority when compared to players born in the Third and Fourth quarter. Key words: Relative Age, Sports, Soccer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".