Off-Ice Resisted Sprints Best Predict All-Out Skating Performance in Varsity Hockey Players
Bibliographic record
Abstract
ABSTRACT: Thompson, KM, Safadie, A, Ford, J, and Burr, JF. Off-ice resisted sprints best predict all-out skating performance in varsity hockey players. J Strength Cond Res 36(9): 2597-2601, 2022-Off-ice fitness testing is commonly used to predict the physiological abilities of ice-hockey players. Although there is a notable association between certain off-ice tests of jump power and anaerobic capacity with on-ice skating acceleration ( r = 0.3-0.7), it is likely that off-ice tests which more closely resemble the demands of skating will have better predictive ability of this skill. The aim of the current study was to compare the suitability of common off-ice fitness tests and off-ice resisted sprints for predicting 15-m on-ice skate time. Male and female varsity-level hockey players performed a battery of common off-ice fitness tests, resisted sprints, and on-ice 15-m sprints over 3 testing days. At least moderate correlations between off-ice tests and on-ice sprints were observed for all common fitness tests (all p ≤ 0.002): Wingate peak power ( r = -0.65), Wingate fatigue rate ( r = -0.53), vertical jump ( r = -0.52), and broad jump ( r = -0.61), with resisted sprint tests showing the strongest associations (off-ice 15-kg resisted sprint ( r = 0.79) and off-ice 30-kg resisted sprint ( r = 0.74)). In multivariate analysis, stepwise regression revealed the 15-kg resisted sprint as the sole meaningful predictor of on-ice sprint time ( R = 0.79, R2 = 0.62; p ≤ 0.001). We conclude that resisted off-ice sprints have better predictive ability of on-ice skate time compared with commonly used off-ice tests. Resisted sprinting can be used by strength and conditioning staff as an indicator of on-ice acceleration ability during periods of limited access to on-ice facilities or as a component of fitness testing.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".