Investigating the Relevance of Maximal Speed and Acceleration in Varsity-Level Female Ice Hockey Players
Bibliographic record
Abstract
PURPOSE: To characterize and compare female ice hockey players' peak skating speed and acceleration ability during linear sprints and gameplay. We also sought to quantify the time spent at various speeds and the frequency of accelerations at different thresholds during games. METHODS: Seventeen varsity-level female ice hockey players (20 [1.4] y, 68.9 [4.9] kg, 167.6 [4.7] cm) participated in an on-ice practice session (performing 3 × 40-m linear sprints) and 4 regular-season games while being monitored using a local positioning system. Speed and acceleration were recorded from the sprint and within-game monitoring. Time on ice spent in relative skating speed zones and the frequency of accelerations at different intensities were recorded. RESULTS: Players' greatest peak speeds (29.5 [1.3] vs 28.3 [1.1] km/h) and accelerations (4.39 [0.48] vs 3.34 [0.36] m/s2) reached during gameplay were higher than those reached in linear sprinting (both P < .01). Peak in-game values were moderately predicted by linear sprint values for speed (r = .69, P < .01) but not for acceleration (r < .01, P = .95). Players spent little time at near-peak linear sprint speeds (≥80% [22.7 km/h], ∼3% time on ice; ≥90% [25.5 km/h], <1% of time on ice) during gameplay. However, 26% to 35% of accelerations recorded during the 4 games were ≥90% of linear sprint acceleration. CONCLUSIONS: Although skating speed may be advantageous in specific game situations, our results suggest that players spend little time at near-maximal speeds while accelerating frequently during games. This warrants further investigation of direction changes, skating transitions, repeated sprints, and other determinant variables potentially related to on-ice success and the implementation of training strategies to improve repeated acceleration or qualities beyond maximal skating speed.
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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 | 0.000 | 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 | 0.000 |
| 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".