Associations between skating mechanical capabilities and off‐ice physical abilities of highly trained teenage ice hockey players
Bibliographic record
Abstract
Abstract This study examines the associations between force and velocity characteristics of forward skating and off‐ice speed, agility, and power of highly trained teenage ice hockey players. Players attending the Quebec ice hockey federation's off‐season evaluation camp were invited to participate in this study. Final sample consists of 107 highly trained teenage ice hockey players (Males: n = 38; 13.83 ± 0.38 years; Females: n = 69: 14.75 ± 0.90 years). Individual force–velocity profiles (F–V) were determined during a 44 m skating sprint. Off‐ice speed, agility, and power were measured using 30 m sprint, 5‐10‐5 agility, and standing long jump. Associations between F–V mechanical capabilities and off‐ice indicators were analyzed with correlational analyses and multivariate analysis of covariance (MANCOVA). Results of pooled data indicate that the three off‐ice measures had moderate associations with F0 and V0 and large associations with Pmax. Associations with Rfmax, Drf, and Sfv were moderate to small. F0 had stronger associations with off‐ice performance in female players while V0 was more important with male players. MANCOVA identified 5‐10‐5 times as the better predictor for F0 while 30 m sprints times better predicted V0. To maximize physical attributes of skating ability, practitioners are encouraged to focus on a general physical preparation for highly trained teenage players. Prioritizing types of exercises that use change of direction or acceleration and linear speed should have distinct effects on F0 and V0 on the ice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".