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Record W1552349969

The effect of hockey stick stiffness and energy transfer on puck velocity for wrist and slap shots

2013· article· en· W1552349969 on OpenAlexaffvenue
Rosemary Grover

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2013
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStiffnessDeflection (physics)Shot (pellet)Structural engineeringStrain gaugeEngineeringMaterials sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Constructing a hockey stick shaft from composite materials has allowed for altering the stiffness of the stick, thereby enabling increased storage and return of elastic energy. However, previous studies examining the effects of shaft stiffness on performance have had mixed results [1,2]. In these studies, stick stiffness appeared to influence the storage and return of energy but the athlete’s ability to transfer this energy from the stick to the puck was not optimized. It is believed that the stick-puck contact interface may be a critical link between this energy transfer. Therefore, the purpose of this study was to determine the effects that stick stiffness, shaft deformation, blade-puck contact time and energy transferred from the stick to the puck have on maximum puck velocity. METHODS AND MATERIALS 22 ice hockey players performed eight slap shots and eight wrist shots with three composite Easton Synergy ST sticks of varying stiffness; 85 flex (6400 N/m), 100 flex (7400 N/m) and 110 flex (8000 N/m). This report is based on results from 14 subjects. Players performed shots on a synthetic ice surface into a hockey net approximately five meters away. Two 350 ohm resistance strain gauge sensors were attached to each shaft to measure the stick deflection during each shot and to calculate the total energy storage and return of the stick. Six force sensors were placed evenly beneath the taped blade to measure the contact time between the stick and puck. The puck velocity during each shot was measured using a Stalker ATS professional radar gun. All data was analyzed using custom made software (MATLAB 2012a, Mathworks). The average of the eight trials for each subject was compared between conditions using a repeated measures ANOVA at a significance level of α=0.05. RESULTS On average, stick stiffness had an influence on puck velocity for both the wrist and slap shot. For the wrist shot, the most flexible stick resulted in a 2.7% higher velocity  (α<0.05) and a 28.3% greater peak deflection (α<0.05) than the stiffest stick. For the slap shot, the stiffest stick resulted in a 3.2% greater puck velocity (α<0.05) and a 11.3% lower shaft deflection (α<0.05) than the most flexible stick. Eight athletes performed their best wrist shots with the 85 flex stick and 11 performed their best slap shots with the 110 flex stick. Optimal stick stiffness varied among subjects. Athletes were therefore divided into groups based on their best and worst stiffness in order to analyze their performance (Figure 1). Under these groupings, significant differences were seen in puck velocity (for both wrist and slap shots) and peak shaft deflection (for wrist shots) while a trend of increased impulse for both the wrist and slap shot was also seen. Figure 1. Results from athletes’ best (blue) and worst (red) stick stiffness for the wrist and slap shot (n=14). Best stiffness was defined as the stick in which the player had the highest puck velocity. DISCUSSION AND CONCLUSIONS This study found that stick shaft stiffness can influence puck velocity during both a wrist and slap shot. When athletes were grouped based on their best and worst stiffness of stick, a trend was seen indicating that when an athlete is shooting with a stick of optimal stiffness, the impulse that the stick imparts on the puck is increased. This increased impulse may be due to the timing of the release of energy from the stick to the puck. To optimize this energy transfer, future studies may consider using blade-puck contact information to provide further insight into the relationship between the timing of puck release and a deflected stick’s returns to equilibrium. REFERENCES Worobets, J.T., et al. Sports Eng, 9 , 191-200, 2006. Hannon, A., et al. Sports Eng , 47, 57-65, 2011.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.272
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations1
Published2013
Admission routes2
Has abstractyes

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