The Relationship Between Lower-Body Force–Time Variables and Skating Performance in Female Ice Hockey Players
Bibliographic record
Abstract
PURPOSE: Ice hockey is a team invasion sport characterized by repeated high-intensity skating efforts, technical and tactical skill, physical contact, and collisions requiring considerable levels of muscular strength. The purpose of this study was to evaluate the relationships between lower-body vertical force-time metrics and skating qualities in subelite female ice hockey players. METHODS: A cross-sectional cohort design was employed utilizing 14 athletes (body mass = 66.7 [1.8] kg; height = 171.6 [6.2] cm; age = 21.1 [1.7] y). The relationships between metrics of lower-body strength collected from a drop jump, squat jump, countermovement jump, loaded countermovement jump, and an isometric squat and 4 skating qualities collected from a linear sprint, repeated sprint test, and a multistage aerobic test were evaluated. RESULTS: The regression models revealed a positive relationship between relative peak force in the isometric squat and skating multistage aerobic test performance (r2 = .388; P = .017) and a positive relationship between repeated-sprint ability and eccentric mean force during the loaded countermovement jump (r2 = .595; P = .001). No significant relationships were observed between strength metrics and skating acceleration or maximal velocity. CONCLUSIONS: These data suggest that skating ability is most affected by relative isometric strength in female ice hockey players. It is recommended that practitioners focus training on tasks that improve relative force output. It is also recommended that isometric relative peak force be used as a monitoring metric for this cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".