Exploring associations between sprinting mechanical capabilities, anaerobic capacity, and repeated-sprint ability of adolescent ice hockey players
Bibliographic record
Abstract
Introduction Sprinting ability and anaerobic capacities are the determinant variables of the performance of ice hockey players. Recent research in sprinting showed the existence of distinct force–velocity (F–V) profiles, but the link between these profiles and anaerobic capacities remains unclear. This study explores the associations between F–V variables and anaerobic capacities among cohorts of highly trained adolescent ice hockey players. Methods Data from 36 men (age, 15.1 ± 0.2 years) and 34 women (age, 16.5 ± 0.7 years) were collected during off-season camps. All athletes completed a 30-m sprint test, a Wingate anaerobic test (WAnT), and a repeated-sprint anaerobic (RSA) test. F–V variables were calculated from the 30-m sprint test. Partial Pearson correlation coefficients for pooled data and Pearson correlation coefficients for individual male and female datasets were calculated. Results Among the F–V variables, maximal theoretical velocity and power were moderately to largely associated with WAnT and RSA performance (|r| = 0.30–0.70). Maximal theoretical force was moderately associated with the RSA (r = −0.32 to −0.33). Discussion The results indicate the importance for highly trained adolescent players to be able to apply force at high velocities to maximize anaerobic capacities. Important differences between male and female players suggest training priorities may differ according to sex.
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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 | 0.001 |
| 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".