The relationship between maturity status, chronological age and talent identification in youth hockey
Bibliographic record
Abstract
During adolescence early maturing individuals are likely to have sporting performance advantages over late maturing individuals of the same chronological age because of maturity related increases in size, strength, speed and endurance. The present study explores the relationship between chronological age, maturity status and talent identification in youth hockey. 619 male (14- 15 yrs) and 385 female (12- 16 yrs) Saskatchewan hockey players attended provincial try-outs (Try-out #1) of which 281 and 137 participated in the study, respectively. Of the male hockey participants, 208 were not selected at Try-out #1, 51 were selected at Try-out #1 but not selected at Tryout #2 and 22 were selected at Try-out #2. Of the female hockey participants, 77 were not selected at Try-out #1 , 40 were selected at Try-out #1 but not selected at Try-out #2 and 20 were selected at Try-out #2. 93 male and 119 female age matched subjects from the longitudinal Saskatchewan Pediatric Bone Mineral Accrual Study (1991-1997) acted as controls. All of the participants had their height, sitting height and body mass measured. Age at peak height velocity (PHV), an indicator of physical maturity, was calculated in the controls and predicted in the hockey participants. Age at menarche was recalled in females only. Data were analyzed using one-way ANOVA, logistic regression and a Kolmogorov Smirnov test. Alpha level was set at p0.05) through the 12 months of the selection year. Results from this investigation suggest that talent evaluators preferentially selected early maturing and chronologically older male hockey players. The same bias, however, was not demonstrated in female hockey.
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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 | 0.002 |
| 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".