Evaluation of Jump Protocols to Assess Leg Power and Predict Hockey Playing Potential
Bibliographic record
Abstract
The purposes of this study were (a) to determine the measurement device and jumping protocol most appropriate for testing the leg power of elite hockey players and (b) to assess the relationship of leg power measurements to hockey playing ability as indicated by draft selection order. Comparisons were made of leg power measurements from the top 95 players entering the National Hockey League Entry Draft using 2 devices (Vertec and Just Jump) and 2 jump protocols (countermovement and squat). Players' leg powers were ranked from highest to lowest power using each device and protocol and were correlated with draft selection order. Vertec leg power measurements were highest (5,511-5,631 W), but there were no significant differences in power between the 2 jumping protocols on either device. Vertec squat jump provided the highest correlation (0.47) between leg power ranking and selection order and was judged to most closely approximate the full-body coordinated movements involved in hockey. The Vertec device using a squat jump protocol is most appropriate for coaches and fitness specialists to use when evaluating hockey potential based on the off-ice leg power measurements of elite hockey players.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".