EFFECT OF BIRTH DATE ON PLAYING TIME DURING INTERNATIONAL HANDBALL COMPETITIONS, WITH RESPECT TO PLAYING POSITIONS
Bibliographic record
Abstract
While a relative age effect (RAE) has been reported in handball, such analyses do not consider actual playing time during competitions, which may actually have more impact on performance in matches. The objective of the present study was to examine the RAE on playing time during international competitions with respect to playing positions. Team compositions (477 players) of the quarter finalists of the 2012 Olympic Games, 2013 World Championships, and 2014 European Championships were analyzed. Month and year of birth where collected in the starting list of each team for center, left and right backs, left and right wings, goalkeepers and pivots. Players were categorized into birth quartile (Q1 Jan–Mar; Q2 Apr–Jun; Q3 Jul– Sep; and Q4 Oct–Dec) and as odd/even year. Playing times were retrieved from official statistics. Data were analyzed for practical significance using magnitude-based inferences. We observed a strong selection bias towards players born earlier within a two-year selection period for all playing positions (Chi-square, p<.001). There was, however, an inconsistent effect of age (i.e. expected, reversed or a lack of it) on actual playing time during competitions. In conclusion, the present study showed for the first time that, despite its large effect on players’ selection, players’ relative age had a limited and position-dependent effect on their actual playing time during top-level competitions. Present findings suggest that the reasons supporting the relative age effect with respect to team selection are at odds with the current utilization of players by coaches in the field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".