Relationship of Physical Fitness Test Results and Hockey Playing Potential in Elite-Level Ice Hockey Players
Bibliographic record
Abstract
The primary purpose of this study was to determine the fitness variables with the highest capability for predicting hockey playing potential at the elite level as determined by entry draft selection order. We also examined the differences associated with the predictive abilities of the test components among playing positions. The secondary purpose of this study was to update the physiological profile of contemporary hockey players including positional differences. Fitness test results conducted by our laboratory at the National Hockey League Entry Draft combine were compared with draft selection order on a total of 853 players. Regression models revealed peak anaerobic power output to be important for higher draft round selection in all positions; however, the degree of importance of this measurement varied with playing position. The body index, which is a composite score of height, lean mass, and muscular development, was similarly important in all models, with differing influence by position. Removal of the goalies' data increased predictive capacity, suggesting that talent identification using physical fitness testing of this sort may be more appropriate for skating players. Standing long jump was identified as a significant predictor variable for forwards and defense and could be a useful surrogate for assessing overall hockey potential. Significant differences exist between the physiological profiles of current players based on playing position. There are also positional differences in the relative importance of anthropometric and fitness measures of off-ice hockey tests in relation to draft order. Physical fitness measures and anthropometric data are valuable in helping predict hockey playing potential. Emphasis on anthropometry should be used when comparing elite-level forwards, whereas peak anaerobic power and fatigue rate are more useful for differentiating between defense.
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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 | 0.004 |
| 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 | 0.001 | 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".