Whole-body predictors of wrist shot accuracy in ice hockey: a kinematic analysis
Bibliographic record
Abstract
The purpose of this study was to identify joint angular kinematics that corresponds to shooting accuracy in the stationary ice hockey wrist shot. Twenty-four subjects participated in this study, each performing 10 successful shots on four shooting targets. An eight-camera infra-red motion capture system (240 Hz), along with passive reflective markers, was used to record motion of the joints, hockey stick, and puck throughout the performance of the wrist shot. A multiple regression analysis was carried out to examine whole-body kinematic variables with accuracy scores as the dependent variable. Significant accuracy predictors were identified in the lower limbs, torso and upper limbs. Interpretation of the kinematics suggests that characteristics such as a better stability of the base of support, momentum cancellation, proper trunk orientation and a more dynamic control of the lead arm throughout the wrist shot movement are presented as predictors for the accuracy outcome. These findings are substantial as they not only provide a framework for further analysis of motor control strategies using tools for accurate projection of objects, but more tangibly they may provide a comprehensive evidence-based guide to coaches and athletes for planned training to improve performance.
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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 | 0.002 |
| 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".