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Whole-body predictors of wrist shot accuracy in ice hockey: a kinematicanalysisYannick Michaud-Paquettea; Patrick Mageea; David Pearsalla; René Turcottea

a Department of Kinesiology & Physical Education, McGill University, Montreal, Quebec, Canada

Online publication date: 07 March 2011

To cite this Article Michaud-Paquette, Yannick , Magee, Patrick , Pearsall, David and Turcotte, René(2011) 'Whole-bodypredictors of wrist shot accuracy in ice hockey: a kinematic analysis', Sports Biomechanics, 10: 1, 12 — 21To link to this Article: DOI: 10.1080/14763141.2011.557085URL: http://dx.doi.org/10.1080/14763141.2011.557085

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Whole-body predictors of wrist shot accuracy in ice hockey:a kinematic analysis

YANNICK MICHAUD-PAQUETTE, PATRICK MAGEE, DAVID

PEARSALL, & RENE TURCOTTE

Department of Kinesiology & Physical Education, McGill University, Montreal, Quebec, Canada

(Received 27 August 2010; accepted 4 January 2011)

AbstractThe purpose of this study was to identify joint angular kinematics that corresponds to shootingaccuracy in the stationary ice hockey wrist shot. Twenty-four subjects participated in this study, eachperforming 10 successful shots on four shooting targets. An eight-camera infra-red motion capturesystem (240 Hz), along with passive reflective markers, was used to record motion of the joints, hockeystick, and puck throughout the performance of the wrist shot. A multiple regression analysis was carriedout 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. Interpretationof 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 armthroughout the wrist shot movement are presented as predictors for the accuracy outcome. Thesefindings are substantial as they not only provide a framework for further analysis of motor controlstrategies using tools for accurate projection of objects, but more tangibly they may provide acomprehensive evidence-based guide to coaches and athletes for planned training to improveperformance.

Keywords: Motion analysis, kinematics, performance, techniques, biomechanics, ice hockey, accuracy

Introduction

Fundamental to the game of ice hockey is the stick, consisting of a straight beam structure

approximately 140 to 160 cm long with a rectangular cross-sectional geometry

(,3.2 £ 1.9 cm) designated as the shaft. The end of that shaft is fused with a slightly

curved 30 cm long blade that is almost perpendicular to the shaft. The hockey stick, as an

extension of the hockey player’s arms and hands, is used for a variety of puck control tasks

such as checking (take away from opponent), intercepting and distributing passes and

maneuvering in tandem while skating and shooting at the opponent’s net. Originally hewn

entirely of wood, modern sticks range from inexpensive wood epoxy laminates to composite

fiberglass materials, the latter now comprising about 70% of the market share.

ISSN 1476-3141 print/ISSN 1752-6116 online q 2011 Taylor & Francis

DOI: 10.1080/14763141.2011.557085

Correspondence: Yannick Michaud-Paquette, Department of Kinesiology, McGill University, 475 Pine Ave West, Montreal,

Quebec, H2W 1S4 Canada. E-mail: yannick.michaud-paquette@mail.mcgill.ca

Sports Biomechanics

March 2011; 10(1): 12–21

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The two most commonly used shooting techniques in ice hockey are the wrist and

slap shots. The wrist shot is generally considered the more accurate of the two with typical

velocities of 20 m/s (72 km/h) compared to the slap shot at 30 m/s (108 km/h) (Woo et al.,

2004). With a shorter swing movement required, the wrist shot is effective for quick

execution. With the high pace of play, the ability to release the puck with minimal swing

movement, maximum velocity and accuracy is a highly valuable skill. This skill like many

others is often learned by emulation of high-caliber players, and by way of trial-and-error

practice. However, the precise motor control strategies used to yield the optimal

performance outcome are not well defined. To date, focus has mainly been given to

slap shots (Dore & Roy, 1976; Pearsall et al., 1999; Polano, 2003; Wu et al., 2003; Woo et al.,

2004; Lomond et al., 2007) whereas few studies have examined whole body technique of the

wrist shot to identify kinematic traits that influence puck velocity (Worobets et al., 2006) or

accuracy.

Kinematic markers can identify functional differences in movement strategies used in

target-related tasks (Button et al., 2003), for example, in elite field hockey players the

drive shot upper-limb kinematics were characterized by inter-limb dissociations referred to

as a bi-pendular motion of the arms (Bretigny et al., 2008). As well, in a recent study by

Michaud-Paquette, Pearsall, and Turcotte (2009) the study of the stick’s spatial path and

orientation during wrist shots, demonstrated characteristic kinematics differences

between low- and high-caliber players that in turn were strong predictors of wrist shot

accuracy. Unknown, however, is how specifically the stick’s path is manipulated by the

player hands and body movements. Hence, the objective of this study is to identify variables

that predict wrist shot accuracy by studying three-dimensional whole-body kinematic

analysis.

Methods

Two one-piece carbon-fiber, composite Bauer Vapor XXXX (Bauer Hockey Corp,

St-Jerome, Canada) hockey sticks with a P92 blade and an 87 flex shaft were used in the

testing protocol. Both sticks were instrumented with six reflective markers (9 mm in

diameter) along the shaft (Figure 1). A sample of 24 healthy male subjects was recruited

representing a cross-section of hockey players ranging from high accuracy to low accuracy

shooters. At the time of testing, subjects did not present any physical injuries that could

prevent them from performing the proposed protocol. Fifteen subjects were right-handed

shooters and 10 were left-handed. All 3D data for right-handed shooters were subsequently

transformed to left-handed data to facilitate data comparison and analysis. Subjects had

Figure 1. Ice hockey stick and puck instrumented with passive reflective markers.

Whole-body predictors of wrist shot accuracy 13

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levels of ice hockey experience varying from recreational to university (Canadian

Interuniversity Sport–CIS), as well as professional.

An eight-camera, Vicon MX system (Viconw, Oxford, UK) was used to record kinematic

data from the subjects’ whole body, sticks and puck. A frame rate of 240 Hz was used in this

study since it was previously determined to yield the appropriate temporal resolution to

capture the movement speed of reflective markers on the subject, stick and puck (Michaud-

Paquette et al., 2009). Experiments were conducted in the McGill biomechanics laboratory

on a synthetic (Vikingw, Toronto, Canada) ice surface of 60.8 m2 To simulate the low friction

surface of natural ice, the synthetic surface was sprayed with a silicone-based lubricant

(Stidwill et al., 2010). Four targets (each 0.3 m £ 0.3 m) were framed by a durable wood

surface covering the hockey net (Figure 2). One reflective marker was fastened to the puck so

that its trajectory and velocity could be obtained (Figure 1). Since the main focus of this

research was whole-body kinematics, a modified marker configuration was chosen

(Michaud-Paquette et al., 2009) to record stick kinematics. Markers were placed on the

subjects according to Vicon’s Plug in Gait with forearm and upper arm model (Viconw,

Oxford, UK).

For all trials, the puck’s starting position was set 4 m from the shooting targets. The

subjects wore their own skates, no hockey gloves and were provided an instrumented hockey

stick, corresponding to their handedness. The corners of the hockey net were divided into

four distinct targets, top ipsi, top contra, bottom ipsi and bottom contra (respectively TI,

TC, BI and BC). One of the four targets was identified for the shooter to aim at prior to each

shot, and the order of target identification was randomized using a randomization matrix.

Once acclimated to the stick provided, the subjects were asked to perform 10 successful shots

with a maximum of 20 attempts per target to establish an accuracy score ((Successful

shots/Total shots) £ 100). A successful trial consisted of a wrist shot that passed through the

identified target. The subjects were verbally instructed to start from a comfortable position

with shoulders perpendicular to the net’s plane and attempt to hit the target as many times as

possible in the specified protocol with near maximal velocity. Additionally, the subjects were

told to only “draw the puck” in the forward and/or lateral direction in each trial.

Reconstructed three-dimensional coordinates of all reflective markers were recorded using

Viconw Nexus 1.3. The recorded spatial coordinates were subsequently used to calculate

relative segment orientation angles as well as global coordinate orientation angles. Joint

kinematics were calculated using the Plug in Gait’s calculation protocol (Viconw,

Figure 2. Experimental setup with subject wearing passive reflective markers. Targets are identified as bottom contra

(BC), bottom ipsi (BI), top contra (TC) and top ipsi (TI).

Y. Michaud-Paquette et al.14

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Oxford, UK) based on the Newington-Helen Hayes gait model that calculates joint

kinematics from the XYZ marker positions and specific subject anthropometric

measurements. These data were then exported to a Matlab (MathWorks Inc., Natick,

MA, USA) readable format to measure gross blade orientation (Cr, Fr, br) in order to

quantify the behavior of the ice hockey stick during the execution of the stationary wrist shot

(Michaud-Paquette et al., 2009). The linear displacement from the puck marker was used to

calculate the puck’s trajectory and derivate its linear velocity (m/s). Shot initiation (SI) was

recognized when the puck started to move in the forward direction, the shot release (SR) was

defined as the instant when the puck’s maximum velocity was reached (signifying cessation of

external contact to the stick’s blade), and the shot end (SE) was calculated as the point where

the linear velocity of the stick changed directions in the y-axis.

Multiple regression analysis was performed and applied to the dependent variable

(accuracy) as well as the independent variables (Table I) to determine which variables best

predicted accuracy. Kinematic variables were analyzed at shot release (SR) as well as the

delta (D angle ¼ change in angle) angles for these variables from SI to SR. All statistical

analyses were performed using SPSSq 17.0 (SPSS Inc., Chicago, IL, USA) and Matlabw

software. Furthermore, a one-way ANOVA was performed to examine differences in the

mean accuracy scores between the different target conditions.

Accuracy scores were calculated as the percentage of successful trials in which subjects hit

the identified shooting targets. These scores were generated and summarized for each

shooting condition as well as for the overall scores. Table II provides a summary of

descriptive statistics for all four shooting targets along with Pearson correlation coefficients

versus overall accuracy scores. Prediction models of accuracy by target were resolved, using

observed body and stick kinematics at shot release as well as kinematic changes between shot

initiation (SI) to shot release (SR). A one-way ANOVA was performed to address the

potential interaction effects between the mean accuracy scores and the accuracy scores for

each of the four targets.

Results

From the multiple regression analysis, predictors of shooting accuracy were identified and

are presented in Table III. Step wise multiple linear regression analysis identified key

variables accounting for 61.3% to 94.5% of variance (Table III) for bottom targets and for

73.2% to 97.7% of variance for top targets. The R 2 values show the individual contributions

of the variables entered. The one-way ANOVA revealed significant differences between the

mean accuracy scores for the top targets (TC, TI) compared to the bottom targets (BC, BI)

( p # 0.05). No significant differences were found across shooting sides.

Table I. Joint motion variables.

Joint Motion (þ) / (-) Joint Motion (þ) / (-) Joint Motion (þ) / (-)

Ankle Dorsiflexion/Plantar flexion Pelvis Flexion/extension Shoulder Flexion/extension

Inversion/Eversion Abduction/adduction Abduction/adduction

Internal/external rotation Internal/external rotation Internal/external rotation

Knee Flexion/extension Spine Flexion/extension Elbow Flexion/extension

Abduction/adduction Abduction/adduction Abduction/adduction

Internal/external rotation Internal/external rotation Internal/external rotation

Hip Flexion/extension Thorax Flexion/extension Wrist Flexion/extension

Adduction/abduction Abduction/adduction Radial/Ulnar deviation

Internal/external rotation Internal/external rotation Pronation/Supination

Whole-body predictors of wrist shot accuracy 15

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Discussion and implications

Similar to the findings of Michaud-Paquette et al. (2009), regardless of the subjects’ shooting

accuracy, a main effect was found for target heights; that is, measures for bottom to

top corners were significantly different with mean scores of 63.95% and 40.27%,

respectively. By that account, the average 23% “handicap” for top corners implies that a

greater motor control challenge exists for the players. Shooting at upper targets requires an

additional compensation for elevation (yaw) in addition to lateral (yaw) trajectory and

forward distance wherein the target can be intercepted by applying the appropriate

momentum vector to the puck (Michaud-Paquette et al., 2009). Control of stick/blade

Table II. Mean accuracy scores by target and correlation coefficient (r) between targets and overall accuracies.

Parameter Bottom contra Bottom ipsi Top contra Top ipsi Overall

Mean 61.0 66.0 37.4 43.0 49.4

SE 5.3 4.2 4.3 4.1 3.7

Sig. Differences TC, TI TC, TI BC, BI BC, BI N/A

Max 100.0 100.0 90.0 83.0 93.0

Min 5.0 30.0 10.0 5.0 15.0

Range 95.0 70.0 80.0 78.0 78.0

r 0.69 0.54 0.78 0.82 N/A

r 2 0.95 0.52 0.98 0.73 N/A

BC ¼ Bottom contra, BI ¼ Bottom ipsi, TC ¼ Top contra, TI ¼ Top Ipsi

Table III. Multiple stepwise regression results yielding the change in the coefficient of determination for accuracy

predictors (R 2).

Bottom Contra Bottom Ipsi Top Contra Top Ipsi

Trail Ankle Flexion/Extension 243 0.035

D Trail Knee Rotation 0.035

D Trail Hip Flexion/Extension 0.076 0.0152

Pelvis Flexion/Extension

0.147

Pelvis Rotation 0.181

D Pelvis

Flexion/Extension 0.137

D Pelvis Rotation 0.073

Spine Flexion/Extension 0.125

Thorax Abduction/Adduction 0.042

D Thorax Abduction/Adduction 0.027

Trail Shoulder Rotation 0.088

D Trail Elbow Flexion/Extension 0.208

D Trail Wrist Flexion/Extension 0.080

D Trail Wrist Pronation/Supination 0.195

Lead Shoulder Abduction/Adduction 0.087 0.240 0.238

Lead Elbow Flexion/Extension 0.020

Lead Wrist Pronation/Supination 0.389 0.285 0.162

Overall R 2 0.945 0.613 0.977 0.732

Lead side of body represents the leg and arm closest to target, while the trail side represents the leg and arm furthest

from target, angle values were measured at shot release

D ¼ change in angle from shot initiation to shot release

Y. Michaud-Paquette et al.16

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orientation ultimately is determined by coordinated movements of the two hands and upper

limb joints as well as body posture. The goal of this study was to determine those body

movements that corresponded to greater shooting accuracy.

To achieve this, a body model of 14 major joints was employed and their 3D movements

recorded during shot execution. A regression analysis identified the fundamental joints and

their modulated kinematics that best predict wrist shot accuracy (Figure 3). Surprisingly,

considering up to 42 degrees of freedom in the body model, only a small number of variables

(between 3 and 9) were needed to account for 61.3% to 97.7% of the accuracy score

variances (Table III). However, these findings are supported by observations that humans

tend to constrain the number of degrees of freedom in order to increase accuracy (Glazier

and Davids, 2009).

The kinematic variables identified varied with targets location and were drawn from the

different body regions. Nonetheless, some kinematic variables were common to most target

(Figure 3). For instance, frequent lower-body variables predicting accuracy were the trail

ankle, knee and hip, particularly for contra-lateral targets. These variables may be related to

weight transfer behavior as the player’s body weight is shifted from the trail to lead limb

during the task’s execution. Characteristic of other sports such as golf drives, field hockey

shots and baseball pitches, weight transfer is part of the summation of segmental

Figure 3. Summary of the fundamental joints and their modulated kinematics (lighter joint and segments) that best

predict wrist shot accuracy for all four shooting conditions: top contra (A), top ipsi (B), bottom contra (C) and

bottom ipsi (D).

Whole-body predictors of wrist shot accuracy 17

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accelerations from the legs to trunk core to upper extremities, such that optimal speed

and trajectory of a projectile may be achieved (Milburn, 1982; Welch et al., 1995; Bretigny

et al., 2008).

The second functional role of the trail leg joints’ movements may be related to maintaining

postural stability. Alpini, Hahn, and Riva (2008) highlighted the unique postural challenge

that a hockey player faces, stating that maintaining postural stability involves coordination of

the limbs, the trunk and head by means of a sensorimotor antigravity network.

Compromised stability may negatively affect movement control of the superior segments.

Dynamic stability is hampered further by the low surface friction of ice. Unlike golf and

Figure 4. Greater extension of the trail hip may serve to counteract the anterior thrust of the upper limbs and stick

upon shot follow through as the upper limbs rotate anticlockwise while the trail leg rotate clockwise for left-handed

shooters.

Figure 5. Representative comparison of a low accuracy (lighter) and a high accuracy shooter (darker).

Y. Michaud-Paquette et al.18

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50A

B

C

45

40

35

30

25

20

150

140

130

120

110

100

90

80

700 20 40 60 80 100

0 20 40

Percent of shot (%)

Lead shoulder

Lead elbow

Percent of shot (%)

HC SDLC SDLCHC

HC SDLC SDLCHC

Ang

le (

degr

ees)

(+)

Add

uctio

n, (

-) A

bduc

tion

Ang

le (

degr

ees)

(+)

Fle

xion

, (-)

Ext

ensi

on

0

10

20

30

40

50

–100 20 40 60 80 100

Lead wrist

Percent of shot (%)

HC SDLC SDLCHC

Ang

le (

degr

ees)

(+)

Rad

ial d

evia

tion,

(-)

Uln

ar d

evia

tion

60 80 100

Figure 6. Lead arm trial-to-trial variability for TC conditions for representative high (HC - solid) and low accuracy

(LC - dashed) caliber shooters for (A) lead shoulder abduction/adduction, (B) lead elbow flexion/extension and

(C) lead wrist radial/ulnar deviation.

Whole-body predictors of wrist shot accuracy 19

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baseball where the players have cleats for surface traction to create sufficient ground torques

to counteract rotational momentum created by upper body and stick swing, hockey players

must find other means to offset such axial momentum. Hence, greater extension of the trail

hip may serve to counteract the anterior thrust of the upper limbs and stick upon shot follow

through (Figure 4).

Several trunk variables were identified as predictors of shot accuracy. Extensive research

on golf drives has characterized rotational behavior of the pelvis and shoulders (thorax) as

part of the aforementioned segmental acceleration sequence (Myers et al., 2009). Further

segmental separation (i.e. decoupling trunk segments) during golf drives is thought to yield

increased storage and utilization of elastic energy in the associated muscles. A similar

behavior may be exhibited during the wrist shot in ice hockey (Woo et al., 2004). Some

variables related to pelvis and thorax orientation were noted as strong predictors of accuracy.

From visual inspection of the data, it may be speculated that lower accuracy shooters showed

greater, poorer control of their postural adjustments and/or less control over the

accumulating segmental acceleration traveling from the base of support to the effecter,

which in this case is the hockey stick. Accurate shooters showed more stability at the trunk

segment from trial to trial while having a more variable control over the arm joints compared

to the less accurate shooters (Figure 5). The following findings confirm findings from other

accuracy studies where the more distal joints control the final trajectory of projectiles (Button

et al., 2003).

With respect to the upper limbs, during the wrist shot the stick blade begins in contact with

the puck, the stick is moved forward in a pushing action to project the puck, and the shot is

terminated by a vigorous pronation of the trail arm about the wrist, and a backward

movement of the lead hand (Wu et al., 2003). The lead forearm pronation-supination

component was a heavily weighted variable in the regression equations for three of the four

shooting conditions; the regression equations suggest that the lead wrist rotation can predict

38.9%, 28.5% and 16.2% of the variance at the BC, BI and TI corners, respectively, which

would directly contribute to appropriate pitch angle of the blade when “scooping” the puck

to the top targets. This previously proposed “scooping” phenomenon results in a

considerable amount of change in the blade’s pitch angle and is needed throughout the

contact phase in order to reach the top targets (Michaud-Paquette et al., 2009). In addition

to this, lead arm elbow and shoulder movements accounted for 55.6%, 28.5%, 55.6% and

59.5% for BC, BI, TC and TI accuracy prediction, respectively. This may suggest a highly

flexible dynamic control of the lead arm’s kinematics to reorient the stick target orientation,

release height and projectile velocity (Button et al., 2003). More specifically, accurate

shooters typically showed less variation in lead shoulder joint kinematics than the more distal

elbow, forearm and wrist joints. Further pattern analysis is needed to verify this inference

(Figure 6).

Conclusion

The objective of this study was to identify the kinematic body segment and joint movement

variables that correspond to wrist shot accuracy. Results showed that shooting accuracy

could be predicted from 61.3% to 97.7% by using 3D analysis to identify where the general

movement pattern of the wrist shot was modulated by target location. Inclusion of body

kinematics accounts for a lot more of accuracy outcome than can be gleamed from the stick’s

path alone. 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 necessary

Y. Michaud-Paquette et al.20

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traits for optimal accuracy. 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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