La fecha de nacimiento y su influencia en el balonmano de élite español: El efecto de la edad relativa
Bibliographic record
Abstract
The classification of athletes based on date of birth generates, in the world of sport, inequalities regarding the number of learning opportunities. This research aims to know how the effect of relative age influences the main Spanish handball leagues. The objective of this study was to know the influence of this effect and determine to what extent the sex and nationality of the players can affect it. To carry out this analysis, the dates of birth, sex and nationality of the 1,141 male and female players from the two main Spanish leagues for the years 2018 and 2021 were analyzed. After classifying them by quarter of birth (n Tri1 = 309 [27, 1%], n Tri2 = 319 [28.0%], n Tri3 = 266 [23.3%], n Tri4 = 247 [21.6%]) and assuming an equitable distribution, descriptive statistics and frequencies were calculated. The chi-square X² test was carried out checking whether or not the date of birth was an advantage when belonging to a team. The results showed no significant differences in either league (p = 0.078 in the men's league and p = 0.129 in the women's league). In this way, it was demonstrated how the effect of relative age affects the same way regardless of sex. On the other hand, regarding nationality, the results showed how the differences were more notable in the case of foreign players, reaching significant differences (p = 0.048). Therefore, despite not finding significant differences in any of the leagues, the differences observed in the total sample, as well as in some of the subgroups analyzed, promote more research and awareness about this effect.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".