Predicting On-Ice Skating Using Laboratory- and Field-Based Assessments in College Ice Hockey Players
Bibliographic record
Abstract
PURPOSE: To determine the predictability of common laboratory/field and novel laboratory tests for skating characteristics in Canadian college ice hockey players. METHODS: A total of 18 male hockey players from the university's varsity hockey team age 20-25 y (height 180.7 [6.4] cm, weight 87.1 [6.7] kg, and body fat 16.2% [4.0%]) completed common laboratory-/field-based testing (ie, standing long jump, vertical jump, off-ice proagility, V˙O2max, Wingate), novel laboratory-based testing (ie, Biodex dynamometer, dual-energy X-ray absorptiometry scan), and on-ice testing (ie, 30-m forward sprint, 30-m backward sprint, on-ice proagility). RESULTS: Pearson correlations and stepwise regression revealed relationships between on-ice forward sprint and 4 off-ice tests (Wingate relative peak power [r = -.62, P < .01], standing long jump [r = -.45, P < .05], off-ice proagility left [r = .51, P < .05], and vertical jump impulse [r = .60, P < .01]). On-ice proagility left was correlated with off-ice proagility left (r = .47, P < .05), Wingate relative peak power (r = -.55, P < .01), and vertical jump impulse (r = -.53, P < .05). The 30-m backward skating test and the on-ice proagility right were not correlated with any off-ice test. CONCLUSION: Commonly used laboratory/field tests are effective in predicting 2 important primary abilities in ice 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".