Do physical maturity and birth date predict talent in male youth ice hockey players?
Bibliographic record
Abstract
The aim of this study was to examine the relationships among biological maturity, physical size, relative age (i.e. birth date), and selection into a male Canadian provincial age-banded ice hockey team. In 2003, 619 male ice hockey players aged 14-15 years attended Saskatchewan provincial team selection camps, 281 of whom participated in the present study. Data from 93 age-matched controls were obtained from the Saskatchewan Pediatric Bone Mineral Accrual Study (1991-1997). During the initial selection camps, birth dates, heights, sitting heights, and body masses were recorded. Age at peak height velocity, an indicator of biological maturity, was determined in the controls and predicted in the ice hockey players. Data were analysed using one-way analysis of variance, logistic regression, and a Kolmogorov-Smirnov test. The ice hockey players selected for the final team were taller, heavier, and more mature (P < 0.05) than both the unselected players and the age-matched controls. Furthermore, age at peak height velocity predicted (P < 0.05) being selected at the first and second selection camps. The birth dates of those players selected for the team were positively skewed, with the majority of those selected being born in the months January to June. In conclusion, team selectors appear to preferentially select early maturing male ice hockey players who have birth dates early in the selection year.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".