Off-Ice Testing Predictive Capabilities of On-Ice Performance Attributes in Men’s Varsity Hockey Players
Bibliographic record
Abstract
Ice hockey is a physically demanding contact sport that requires players to perform repeated bouts of high-energy output with shifts lasting from 30 to 80 seconds. Predicting on-ice performance during a game is difficult. Physical and anthropometric testing has been commonly used in hockey to predict and evaluate fitness attributes (i.e. strength, agility, flexibility), which the hockey community believes are advantageous to several sport-specific tasks in hockey, such as the player’s skating speed and balance. PURPOSE. To explore the relationship between NHL combine testing results and on-ice testing assessments among elite varsity hockey players. METHODS. Twenty-five Men’s Varsity Hockey players from McGill University (age: 22.8 ±1.43, height: 1.81 ±0.06, weight: 87.13, ±6.73, %BF: 16.21 ±4.03) participated in the study. Participants performed the 2015 standard NHL combine tests. On-ice testing was conducted using advanced timing equipment to control for errors. Tests completed by the players were the 30-m forward and backward sprints, transition agility test, weave agility test, and pro-agility test. Six NHL teams currently use this battery of on-ice tests, to replicate game like situations. RESULTS. See attached table for results. CONCLUSION. It can be concluded that most of the NHL combine tests were not correlated with the on-ice measures of performance obtained. Improving the sport-specific nature of dryland testing represents a priority for sport scientists working with elite hockey players.Table 1: Correlations of NHL 2015 combine tests to novel on-ice testing protocol.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".