Relationship Between Hockey Skating Speed and Selected Performance Measures
Bibliographic record
Abstract
The objective of this study was to determine the relationship between specific performance measures and hockey skating speed. Thirty competitive secondary school and junior hockey players were timed for skating speed. Off-ice measures included a 40-yd (36.9-m) sprint, concentric squat jump, drop jump, 1 repetition maximum leg press, flexibility, and balance ratio (wobble board test). Pearson product moment correlations were used to quantify the relationships between the variables. Electromyographic (EMG) activity of the dominant vastus lateralis and biceps femoris was monitored in 12 of the players while skating, stopping, turning, and performing a change-of-direction drill. Significant correlations (p < 0.005) were found between skating performance and the sprint and balance tests. Further analysis demonstrated significant correlations between balance and players under the age of 19 years (r = -0.65) but not those over 19 years old (r = -0.28). The significant correlations with balance suggested that stability may be associated with skating speed in younger players. The low correlations with drop jumps suggested that short contact time stretch-shortening activities (i.e., low amplitude plyometrics) may not be an important factor. Electromyographic activities illustrated the very high activation levels associated with maximum skating speed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".