Importance of Body Composition in the National Hockey League Combine Physiological Assessments
Bibliographic record
Abstract
Chiarlitti, NA, Delisle-Houde, P, Reid, RER, Kennedy, C, and Andersen, RE. Importance of body composition in the national hockey league combine physiological assessments. J Strength Cond Res 32(11): 3135-3142, 2018-The National Hockey League (NHL) combine was designed to assess draft-eligible players based on body composition, speed, power, and strength. The importance of body composition in the battery of combine physical tests was investigated, and differences in results based on position were explored. Thirty-seven elite male Canadian university hockey players (age = 22.86 ± 1.55 years, mass = 87.21 ± 6.52 kg, and height = 181.69 ± 6.19 cm) participated in the study at the beginning of their hockey season. All participants underwent physical testing (as outlined in the 2016 NHL combine) and 1 total body dual energy x-ray absorptiometry scan to measure body composition. Partial correlations (controlling for body mass) were used to explore the relationship among body composition measures (body fat percentage, visceral fat, body mass index, lower lean tissue mass, upper lean tissue mass, upper fat mass, and lower fat mass) with NHL fitness tests (bench press, pull-ups, grip strength, long jump, proagility, vertical jump, V[Combining Dot Above]O2max, and the Wingate Anaerobic Test). In 4 of the 6 strength/power tests (Wingate Anaerobic Test, long jump, bench press, and both grip strengths), lower and upper lean tissue mass explained significant amounts of variance. Although forwards and defensemen significantly differed in right grip strength and proagility left scores, they did not differ in regard to any body composition variables. Body composition has a significant influence on several combine-specific tests, which may help sport scientists and strength and conditioning coaches to better tailor training programs and to optimize performance in elite hockey players.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".