Relationships to Skating Performance in Competitive Hockey Players
Bibliographic record
Abstract
The purpose of this study was to identify off-ice variables that would correlate to on-ice skating sprint performance and cornering ability. Previous literature has not reported any off-ice testing variables that strongly correlate to on-ice cornering ability in ice hockey players. Thirty-six male hockey players aged 15-22 years (mean +/- SD: 16.3 +/- 1.7 years; weight = 70.8 +/- 10.4 kg; height = 175.6 +/- 4.1 cm) with an average of 10.3 +/- 3.0 years hockey playing experience (most at AA and AAA levels) participated in the study. The on-ice tests included a 35-m sprint and the cornering S test. The off-ice tests included the following: 30-m sprint, vertical jump, broad jump, 3 hop jump, Edgren side shuffle, Hexagon agility, side support, push-ups, and 15-second modified Wingate. The on-ice sprint test and cornering S test were strongly correlated (r = 0.70; p < 0.001). While many off-ice tests correlated with on-ice skating, measures of horizontal leg power (off-ice sprint and 3 hop jump) were the best predictors of on-ice skating performance, once weight and playing level were accounted for. These 4 variables accounted for a total of 78% (p < 0.0001) of the variance in on-ice sprint performance. No off-ice test accounted for unique variance in S-cornering performance beyond weight, playing level, and skating sprint performance. These data indicate that coaches should include horizontal power tests of off-ice sprint and 3 hop jump to adequately assess skating ability. To improve on-ice skating performance and cornering ability, coaches should also focus on the development of horizontal power through specific off-ice training, although future research will determine whether off-ice improvements in horizontal power directly transfer to improvements in on-ice skating.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".