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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".