Transfer of Off-Ice Agility to On-Ice Performance in Elite Canadian Collegiate Hockey Players
Bibliographic record
Abstract
In an effort to optimize the physiological adaptations of a training program, specificity in training prescription is necessary. Agility is an important component of any hockey player’s skillset that is often trained off-ice. PURPOSE: Examine the relationship between the off-ice pro-agility and standing long jump tests with agility tests performed on-ice in collegiate hockey players. METHODS: Nineteen elite Canadian university hockey players (age = 22.5±1.4, height = 70.85±2.61 inches, weight = 192.93±16.16 lbs, body fat = 15.82±4.21%) participated in an off-ice physical evaluation and on-ice testing. Players were assessed off-ice with the standing long jump and pro-agility tests with one trial on each side of the body (left and right). Within the same week, players were assessed a second time on-ice using the same pro-agility test, the weave agility test, and the transition agility test. On-ice agility tests are a novel method that specific NHL teams using to test the on-ice performance of their players. All tests, except the standing long jump (measured in cm), assessed time duration using advanced laser technology. RESULTS: There was a negative correlation (r =-.479, p ≤ .05) between the off-ice standing long jump and on-ice weave agility tests. The off-ice pro-agility test executed on the left side was positively correlated (r =.473, p ≤ .05) with the on-ice pro-agility test done on the same side. CONCLUSION: The off-ice pro-agility and standing long jump tests may predict on-ice performance in certain aspects of game play. New off-ice agility tests should be designed that would be more representative of the different types of movements executed on-ice. Teams could consider more on-ice testing to more accurately identify players’ sport specific agility level.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".