A Study of Relative Age Effect in Professional Leagues (The Sample of Turkish Super League, 1st League, 2nd League and 3rd League)
Bibliographic record
Abstract
This present study aims to examine the phenomenon of the relative age effect among football players based on birth year and the positions they played in Turkish professional leagues. A total of 3622 professional football players from Turkish Super League, Spor Toto 1stLeague, 2ndLeague and 3rd League were included in the study. The players were divided into 4 different quarters with 3-month intervals and 2 different half-terms with 6-month intervals starting from January. The data of the second half of the 2021-2022 football season were used in the research. The data of the study were obtained from the official and open-access web pages of the Turkish Football Federation and Transfermarkt. With the chi-square test, the distribution of the football players according to birth months, positions and leagues and the frequency distribution differences between the groups were analyzed. SPSS 22 statistical package program was used to analyze data and the significance level was accepted as p<0.05. As a result, it has been observed that the number of football players born in the first quarter and the first 6 months of the year in all of the professional leagues in Turkey is more than the players born in the other quarters of the year, and the results were found statistically significant. In addition, it has been determined that there are more football players born in the first months of the year in all positions according to the leagues. As a result, the presence of relative age effect in football players playing in professional leagues in Turkey has been revealed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".