The Effect of Blade Alignment on Kinematics and Plantar Pressure during the Execution of Goaltender-Specific Movement Patterns: A Case Study
Bibliographic record
Abstract
Innovations in material properties of goaltender skates have improved the protective characteristics of the boot, leading to redesign of the blade holder to resemble players’ holders. The redesigned blade holder introduces the ability to customize blade alignment, which may grant a performance advantage. We investigated the effect of blade alignment on kinematics and plantar pressure during the execution of two different goaltender-specific movement patterns: (1) the butterfly drop to recovery and (2) the lateral butterfly slide to recovery. The main objective of this study was to investigate the effect of three blade alignment conditions. The secondary objective was to compare two neutral alignment conditions, which was facilitated by studying the effects of two different holders on kinematics and plantar pressure during two goaltender-specific techniques. A male goaltender with professional experience completed an A–B–A design, executing five trials of A, B, and A for both movements with each blade alignment condition (n = 30 per collection, n = 90 overall) on synthetic ice in a controlled lab environment. Blade alignment conditions were defined by the alignment of the blade holder on the boot and the type of blade holder. Kinematic and plantar pressure data were collected simultaneously using 3D motion capture and in-skate pressure insoles, respectively. Increased butterfly drop velocity (2.07 ± 0.09 m/s) and peak plantar pressure (77.19 ± 2.67 psi) were revealed when executing the butterfly drop with medial alignment. This work suggests medial blade alignment may enable the goaltender to drop into the butterfly position faster, potentially increasing the likelihood of making a save.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".