Morphological, Physiological and Skating Performance Profiles of Male Age-Group Elite Ice Hockey Players
Bibliographic record
Abstract
The purpose of this study was to describe the evolution of morphological, physiological and skating performance profiles of elite age-group ice hockey players based on repeated measures spread over one season. In addition, the results of fitness tests and training programs performed in off-ice conditions and their relationship with skating performance were analyzed. Eighteen high level age-group ice hockey players (13.1 ± 0.6 years) were assessed off and on-ice at the beginning and at the end of the hockey season. A third evaluation was also conducted at the beginning of the following hockey season. The players were taller, heavier, and showed bone breadths and muscle girths above the reference population of the same age. Muscular variables improved significantly during and between the two hockey seasons (p < 0.05). However, maximal aerobic power improved only during the off-season. All skating performance tests exhibited significant enhancements during the hockey season, but not during the off-season where some degradation was observed. Finally, weak observed variances (generally <20% of the explained variance) between physiological variables measured off-ice and on-ice skating performance tests indicated important gaps, both in the choice of the off-ice assessment tools as well as in training methods conventionally used. The reflection on the best way to assess and train hockey players certainly deserves to be continued.
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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 | 0.001 |
| 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".