Improving ice hockey slap shot analysis using three-dimensional optical motion capture: A pilot study determining the effects of a novel grip tape on slap shot performance
Bibliographic record
Abstract
There have been significant improvements in ice hockey equipment technology within the last decade; however, little is known about how these improvements are affecting puck mechanics. The purpose of this study was to develop a testing protocol using three-dimensional motion capture to improve ice hockey shot analysis by providing additional information to traditional shot analysis techniques. Then, the feasibility of this protocol was tested by performing a pilot study that analyzes the effects of a new grip tape on slap shot performance. Four elite hockey players performed four slap shots in each of the four conditions: (1) bare hockey stick/normal hockey gloves, (2) traditional hockey tape stick/normal gloves, (3) Greptile™ tape stick/normal gloves and (4) Greptile™ tape stick/Greptile™ gloves. Reflective markers attached to the puck, stick and target were tracked by a three-dimensional optical motion capture system recording at 200 Hz. Linear and angular velocities of the puck, accuracy and puck topple were not different between condition 1 and the remaining three grip conditions; however, there were accuracy differences between conditions 1 and 3. In addition, there were no correlations between angular velocity and shot accuracy, and puck topple and shot accuracy. Due to the small sample size, the effects of Greptile™ tape on slap shot performance must be further tested in order to compare the four conditions with more advanced statistics; however, the developed research strategy, using three-dimensional motion analysis, provides an approach to researchers and companies that are interested in identifying the effects of new hockey equipment/technologies on slap shot performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".