The association of two relative age effects and estimated money value in elite soccer
Bibliographic record
Abstract
Relative age effects, which arise from age differences within youth sport cohorts, seem to influence later monetary values of soccer players (Ashworth & Heyndels, 2007). Additionally, Schorer and colleagues (2013) showed that there are different types of RAEs (i.e., within and between year effects), and that they can interact to influence athlete development. The aim of this study was to investigate whether within and between year effects influence long-term monetary values of male soccer players. Birth dates of male Under-17 2007 and 2009 soccer World Championships participants were obtained via official FIFA websites. Estimated monetary values for 822 were retrieved from the website www.transfermarkt.de. When players with no estimated market value were excluded from non-parametric tests, within year effects became significant, H(3, n = 547) = 8.79, p = .03. Quartile 4 had the highest rankings with 312.64, followed by similar rankings for quartile 1 (MR = 278.03) and quartile 3 (MR = 279.97). Quartile 2 had the lowest mean ranking with 246.13. For between year effects the same pattern was revealed, H(1, n = 806) = 17.66, p < .01. The mean ranks for older cohorts (MR = 278.96) were higher than the younger cohorts (MR = 232.22). While the results of this study show that the within year effect has an association with the estimated money value of players, the between year effect seems to have a stronger association. Future relative age studies need to consider both effects for a better understanding of the consequences of relative age.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".