The Biomechanical Characteristics of Development-Age Hockey Players: Determining the Effects of Body Size on the Assessment of Skating Technique
Bibliographic record
Abstract
Skating ability, and more specifically the ability to accelerate from a stationary position or change direction rapidly, is recognized as one of the most important skills in ice hockey. While coaches may use drills to compare skating performance between individuals, especially during player selection, few studies have identified the essential kinematic variables that contribute to the ability of development-age hockey players to accelerate over a specified distance. Previous research reported that the determination of performance and ultimately skating power could be related to specific biomechanical parameters, especially among developing hockey players. However, there is evidence to suggest that the potential confounding effects of height and weight should be considered in such biomechanical evaluations. Considering the range of variability for the height and weight of ten year-old children, it may be appropriate to include these as predictors of skating performance. The purpose of this study was to evaluate the biomechanical characteristics of minor hockey players while performing an on-ice acceleration skill test. In addition, the study evaluated the contribution of height and weight on the assessment of skating technique. Participants were 30 male development-age hockey players categorized by level of play. Correlation identified the kinematic variables related to time to skate six meters. Regression analysis identified the set of variables that best predicted time to skate six meters. Comparing structural models across studies demonstrates the importance of body size on skating performance. These results illustrate the importance from the development age, through to university athletes, to elite NHL prospects.
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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 | 0.002 |
| 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".