Breaking the ice: exploring sex-based variations in the mechanics of ice hockey slap shots
Bibliographic record
Abstract
The objective of this study was to compare joint angles and spatiotemporal variables between male and female ice hockey players during skating slap shots. Thirty-nine collegiate players (25 men, 14 women) participated. Kinematic data were collected using a Xsens 17-inertial measurement system. Key variables included joint angles for the trunk, upper, and lower extremities, as well as temporal measures for shot execution time, and the backswing, downswing, and follow-through phases. Statistical Parametric Mapping (SPM) was applied to analyse sex-based differences in joint kinematics. Temporal data were compared using two-way ANOVAs. Results indicated that males exhibited longer backswing and downswing phases, contributing to longer overall shot execution times. Males also demonstrated greater trunk flexion and lead shoulder flexion, while females showed more lead shoulder abduction, elbow flexion, and trail wrist extension during the backswing and downswing phases. These findings highlight the influence of anatomical and strength differences on slap shot mechanics. Considering sex-specific biomechanical differences in the development of training regimens and equipment design may enhance performance and development in ice hockey for all.
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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 | 0.000 |
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".