Off-Ice Fitness of Elite Female Ice Hockey Players by Team Success, Age, and Player Position
Bibliographic record
Abstract
This study examined off-ice fitness profiles of 204 elite female ice hockey players from 13 countries who attended a high-performance camp organized by the International Ice Hockey Federation (IIHF) in Bratislava, Slovakia, in July of 2011. Athletes were tested using standardized protocols for vertical jump (centimeters), long jump (centimeters), 4-jump average (centimeters), elasticity ratio (4-vertical jump average/vertical jump), pull-up or inverted row (n), aerobic fitness (V[Combining Dot Above]O2max), body mass (kilograms), and body composition (% fat). These variables were examined relative to team success in major international hockey competition (group 1: Canada and USA, group 2: Sweden and Finland, group 3: All other participating countries), age group (Under 18 and Senior/Open Levels), and player position (forwards, defenders, and goalies). The athletes from countries with the best international records weighed more, yet had less body fat, had greater lower body muscular power and upper body strength, and higher aerobic capacity compared with their less successful counterparts. Compared with the younger athletes, athletes from the senior-level age group weighed more and had higher scores for lower body power, pull-ups, and aerobic capacity. There were no significant differences in anthropometric or fitness data based on player position. This study is the first to report the physical characteristics of a worldwide sample of elite female ice hockey players relative to team performance, age, and player position. Coaches should use these data to identify talent, test for strengths and weaknesses in conditioning programs, and design off-ice programs that will help athletes match the fitness profiles of the most successful teams in the world.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".