Is the date of birth an advantage/ally to excel in handball?
Bibliographic record
Abstract
The Relative Age Effect (RAE) has been analyzed in a population of Spanish international handball players (n=161) divided into four different levels: Senior, Junior, Juvenile and Promising Talents. The variables registered were quarter, half year and year of birth using the initial information of their date of birth. The data were collected from the Royal Spanish Handball Federation on-line data base. The statistical method used was the 2 and the minimum level of significance was set at p<0.05. The total results on distribution by quarter show a significant difference ( 2 = 21.68; p<0.01) with a greater frequency of players born in the first quarter (40.37%) compared to those born in the second (22.36%), third (16.15%) and fourth quarter (21.12%). The total results on the distribution of birth date by half year show a significant difference ( 2 = 10.44; p<0.01) with a greater frequency of players born in the first half of the year (62.73%). With regard to the rate of births registered in an even numbered or odd numbered year there are significant differences when the rates for an even numbered year (64.60%) and an odd numbered year (35.40%) are compared with those of the general population ( 2 = 13.72; p<0.001). Based on the data collected and analyzed it can be concluded that there is a RAE in the basic categories of the Spanish national men's handball teams according to quarter, half year and year of birth (even or odd numbered), but there exists little knowledge about the causes and consequence which may be produced by, or derive from, 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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".