The Importance Of Body Composition In The National Hockey League Testing Combine
Bibliographic record
Abstract
The National Hockey League (NHL) combine was designed to assess draft-eligible players based on speed, power, strength, and physical size. Body composition (anthropometric measures and skin fold values) are recorded for all players, with the belief that it may play a role in physical performance. PURPOSE: To examine the role of body composition in the battery of physical tests and to compare differences in combine results based on position. METHODS: Over two seasons, thirty-seven elite male Canadian university hockey players (age = 22.86 ± 1.55 years, weight = 87.21 ± 6.52 kg, height = 181.69 ± 6.19 cm, body fat percentage = 16.06 ± 3.93%) participated in the study at the beginning of their hockey seasons. All participants underwent physical testing (as outlined in the 2016 NHL combine) and a day after testing, one total body dual energy x-ray absorptiometry (iDXA) scan to measure body composition. RESULTS: Pearson product correlations were used to explore the relationship among anthropometric measures (body fat percentage, visceral fat (kg), height, weight, leg lean mass per kg, upper lean mass per kg, and wingspan) with NHL fitness tests (bench press, maximum pull ups, grip strength, long jump, and Wingate anaerobic test). Multiple linear regression was used to explore the association among regional body composition and NHL combine tests. Upper body lean mass/kg (R2 = .417) explained the most variance in the bench press while height (R2 = .566) explained the most variance in the long jump. Between positions, defensemen displayed greater right grip strength compared to forwards (p<0.05). All other comparisons were non-significant. CONCLUSIONS: There are numerous factors that may influence performance on combine-specific physical tests. Body composition and anthropometric measures both seem to influence combine-specific tests, which may help sport scientists better tailor training programs to optimize performance in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".