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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".