Anticipatory Judgment of Tennis Serve: A Comparison

1. Introduction

Visual information plays a significant role in sportsinvolving interceptive tasks such as catching a baseball(McBeath et al., 1995) or striking a ball with a cricketball (Land and McLeod, 2000). In particular, in sportswhere balls move at high speeds, athletes must reactimmediately to their opponent’s motion and the changesin the surrounding environment. In essence, they needto anticipate future events based on their opponent’spattern of movement. For example, skilled tennisplayers are able to use their opponent’s movement

information to determine the stroke direction and todecrease their response delay times (Shim et al., 2005).In order to ensure accurate and efficient performance,they must perceive the visual information for the taskprior to their opponent making contact with the ball. Infact, previous studies in sports perception have revealedthat skilled players were able to use visual cues to guidetheir anticipatory responses much earlier than lessskilled players (e.g., Abernethy and Russell, 1987;Abernethy et al., 2001; Goulet et al., 1989; Shim et al.,2005; Williams et al., 2002).

A significant amount of research in sports perception

Fukuhara, K., et al.

12 International Journal of Sport and Health Science Vol.7, 12-22, 2009http://www.soc.nii.ac.jp/jspe3/index.htm

Anticipatory Judgment of Tennis Serve:

A Comparison between Video Images and

Computer Graphics AnimationsKazunobu Fukuhara*, Hirofumi Ida**, Seiji Kusubori***

and Motonobu Ishii*

*Department of Human System Science, Tokyo Institute of Technology 2-12-1-W9-601 O-okayama, Meguro, Tokyo 152-8552 Japan

[email protected]**Human Media Research Center, Kanagawa Institute of Technology

1030 Shimo-Ogino, Atsugi-shi, Kanagawa 243-0292 Japan***Prefectural University of Hiroshima

562 Nanatuka-cho, Shoubara-shi, Hiroshima 727-0023 Japan[Received July 14, 2008; Accepted June 26, 2009; Published online December 10, 2009]

This study aimed to explore the feasibility of using computer graphics (CG) animations toevaluate perceptual skills in tennis. In Experiment 1, we used video images or CG animations toexamine the visual search behaviors and the accuracy of anticipating serve direction of 18skilled tennis players. Participants viewed the racket area for a longer time during the 150 msperiod immediately before the moment of racket–ball contact in the video image conditionopposed to the CG animation condition. In addition, the participants made more accuratejudgments in the video image condition than in the CG animation condition. In Experiment 2,we investigated the information pick-up patterns of 10 skilled players while they viewed eitherthe video images or CG animations using a temporal occlusion. Consistent with the results ofExperiment 1, participants made more accurate judgments during the 150 ms periodimmediately before the contact in the video image condition than in the CG animationcondition. The results of both experiments showed that the perceptual information in the 150 msperiod differed between the two film types. However, the anticipation accuracy of the CGanimation condition in both experiments was over the chance level (50%), suggesting that theparticipants were able to pick up the anticipatory information of serve direction from the CGanimations. This led to the conclusion that CG animations would be a valuable tool to examineperceptual skills in tennis.

Keywords: visual search, temporal occlusion, perceptual skills

[International Journal of Sport and Health Science Vol.7, 12-22, 2009]

Paper : Psychology

has studied when, where, and how skilled playersextract visual cues, using approaches such as eye-movement recording (e.g., Kato and Fukuda, 2002;Savelsbergh et al., 2002; Williams et al., 2002),temporal occlusion (e.g., Abernethy et al., 2001;Farrow et al., 2005; Goulet et al., 1989), and spatialocclusion (e.g., Abernethy and Russell, 1987; Shim etal., 2006; Jackson and Mogan, 2007). Typically,experimental paradigms have presented video displaysthat simulate the player’s perspective while he or she isfacing opposing players (e.g., returning a serve intennis or facing a penalty kick in soccer). Thesesimulated visual stimuli were presented to both skilledand less skilled players, whose task was to anticipatethe final outcome of the opponent’s motions, forexample, identifying the direction in which the ballwould be hit. These studies have identified the spatialand temporal visual cues used in anticipatory responsesbased on differences in skill level.

Moreover, previous researchers in sports perceptionhave used point-light displays to examine the minimumessential source of information for skilled performance(Abernethy, 1993; Abernethy et al., 2001; Shim et al.,2005; Ward et al., 2002). The point-light displaysrepresent only the kinematic features of the opponent’smovement pattern; however, both skilled and lessskilled players in racket sports were able to anticipatemovement outcomes from this display (Abernethy etal., 2001; Shim et al., 2006). Ward et al. (2002)investigated the interaction among the visual searchbehaviors, anticipation and biological motionperception in tennis, when skilled and less skilledplayers viewed video images and point-light displays.The authors demonstrated that the accuracy of motionperception was lower when point-light displays wereviewed than when video images were viewed. However,the viewing behavior of skilled players had a moreconsistent pattern across the video images and point-light displays than that of less skilled players. In otherwords, the skilled players extracted similar sources ofinformation from the video images and point-lightdisplays. Thus, the superior information processing ofskilled players was related to the information availablefrom the essential kinematics of their opponent’smotion pattern (e.g., Abernethy et al., 2001; Shim etal., 2006; Ward et al., 2002).

Although research on perceptual expertise is rapidlyexpanding, few attempts have been made to clearlyidentify the key aspects underpinning the perceptualskills of successful athletes (Abernethy et al., 2001). To

this end, it is necessary to precisely manipulate theperformer’s motion and environment to match theobjective of the research. While the point-light displaysare a potentially valuable tool to permit themanipulation of display kinematics, the computer-animated characters also have significant potential forthe simulated presentation of sport situations becausethey allow exact changes to be made in variousparameters, such as the movement, body,characteristics, contour, texture, and perspective of theimage. In recent times, with the development ofcomputer graphics (CG) technology, several sportsscience studies have begun to use humanlike CGanimations as a visual stimulus that plays handball(Bideau et al., 2003; 2004), runs (Hodgins et al., 1998),and plays tennis (Fukuhara et al., 2005; Pollick et al.,2001). The advantage of using CG animations is thatthey can precisely control kinematic features and alsoprovide information about appearance (e.g., contour,texture, and size). For example, Bideau et al. (2004)investigated the reactions of skilled handballgoalkeepers as they faced virtual reality (VR)opponents whose throwing movements were modulatedin three ways (arm movement, rotation of the trunk,ball release time). The authors reported that thealterations in throwing movements resulted insignificant differences in the goalkeepers’ reactions.Importantly, these results suggest that CG charactersand environments can be manipulated for empirical ortraining purposes in sports. It should be possible todefine more clearly the crucial factors underlyingperceptual skills in sports, making CG animations avery useful tool in the study of sports perception(Loomis et al., 1999; Williams et al., 2002).

However, there is an important issue of whether ornot CG animations accurately reproduce perceptualinformation of the real action. In particular, CGanimations are based on artificial geometric models,such as the polygon and the Non Uniform Rational B Spline (NURBS) model (Hodgins et al., 1998). These geometric models consist of mathematicalrepresentations of any three-dimensional object, createdthrough computer simulation. Thus, the informationthat makes up the CG animations and the real situationis different in essence. To examine perceptual skills insports using CG animations, it will be necessary toidentify the nature of perceptual information within CGanimations. To date, although some researchers haveemployed CG animations in sport tasks (e.g., Bideau etal., 2003; 2004; Hodgins et al., 1998; Pollick et al.,

Anticipatory Judgment of Tennis Serve

International Journal of Sport and Health Science Vol.7, 12-22, 2009 13http://www.soc.nii.ac.jp/jspe3/index.htm

2001), no attempts have been made to identify thevisual search behaviors or information pick-up patternsof athletes as they view these simulated sportsituations.

The aim of this study was to explore how CGanimations of tennis serves affected the perceptualskills of skilled players. The participants attempted toanticipate the serve directions from either the videoimages or CG animations. In Experiment 1, wemeasured the eye movements using an eye-pathtracking system to identify the visual search behaviorsbetween two film types (video images and CGanimations). In Experiment 2, we investigated theanticipatory judgment performance using a temporalocclusion method (e.g., Abernethy et al., 2001; Farrowet al., 2005; Goulet et al., 1989) to assess theinformation pick-up patterns across the two film types.We hypothesized that there would be some differencesin the visual search behaviors and/or information pick-up patterns between the video images and CGanimations. From the results of both experiments, weattempted to explore the possible advantages of usingCG animations in sports perception studies.

2. Experiment 1

2.1. Methods

2.1.1. ParticipantsEighteen skilled (M�SD: age, 21.4�1.6 years)

tennis players participated in Experiment 1. The

participants belonged to a college tennis club and hadan average of 7.3 (SD�3.1) years playing experience,during which time they had played an average of 220(SD�93) competitive matches. They trained daily intheir tennis club and competed in national- or regional-level competitions. All participants gave informedconsent before the experiment, and had normal orcorrected-to-normal vision.

2.1.2. Test films

The initial procedure involved the construction oftwo test films, one using video images and one usingCG animations. A professional right-handed tenniscoach (age, 42 years; experience, 30 years) participatedin this process, and was required to serve the ball withmaximum effort. Serving targets were set on the leftand right sides of the service box (see Figure 1). Theserve motions were recorded by a digital video camera(30 Hz, DCR-TRV7, Sony Inc., Tokyo) and two high-speed cameras (250 Hz, HSV-500C3, Nac Inc., Tokyo),which provided the video images and model data forthe CG animations, respectively. The high-speedcameras were used to follow the fast swing motion, andobtain the motion data of an actual serve on an outdoortennis court. The digital video camera was positioned ata height of 1.6 m (receiver’s perspective), at the cross-point of the sideline and the baseline on the receiver’sside of the court, while the two high-speed cameraswere positioned outside the tennis court. For the videoimages, the video camera angle was adjusted such that



Figure 1 A schematic illustration of the videotaping environment.

the captured images included the server’s entire bodyand the tossed-up ball.

Twenty-four successful serves were recorded asoriginal test films, and then we selected six serves(three hitting the left target and three the right) to beincluded in the test films. The selection criterion of thetest films was that the trial had the moment ofracket–ball contact image in the digital video camera tomatch the occlusion point in the video images and CGanimations. Moreover, the video images had thepossibility of including some visual images of ball-flight just after the racket–ball contact because thedigital video camera had a slow frame rate (30 Hz).With this criterion, we attempted to minimize thedifference in the moment of occlusion between thevideo images and CG animations. The video imageslasted for 3.5 s—the first 2.0 s included the still imageof the server’s ready position, and then in the last 1.5 s,the server went from his ready position to the momentof racket–ball contact.

Next, for the CG animations, we edited the high-speed camera images to provide the same successfulserves as those presented in the video images. Manualdigitizing (Frame-DIAS II, DKH Inc., Tokyo) of themotions was accomplished using the edited high-speedcamera images. The digitized data were converted to 3-D coordinate data using a direct linear transformationmethod (Winter, 1990). A total of 28 points were set onthe target objects (23 on the server’s body, 4 on theracket, and 1 on the ball). The total number of digitizedframes was 350, although these were reduced to 42frames to match the number of frames sampled with thedigital video camera at a 30 Hz sampling rate. Theserve motion of the CG animations was developed from3-D coordinate data using CG modeling software(Maya 4.5, Alias Inc., Toronto). In addition, we insertedthe background scene from the video images into theCG animations, with the viewpoint matched to thereceiver’s viewing angle in the video images. In total,we produced 12 test films comprising six serves (threefor the left and three for the right side) for each of filmtype (video images and CG animations).

2.1.3. ProcedureThe test films were projected (LV5100, Canon Inc.,

Tokyo) onto a large screen (1.77 m�0.99 m) positioned4.48 m in front of the participants. The test servemotion was adjusted to 0.5 m in height from the foot tothe top of the racket. This provided a visual angle ofabout 6.4 degrees, which was similar to that of the

serving motion seen on the tennis court. Participantsviewed the test films while sitting with their heads fixedon a chin support to ensure accurate measurement ofeye movements. Movements of the right eye wererecorded with an eye-path tracking system (60 Hz,Eyemark Recorder 8, Nac Inc., Tokyo). Following a 9-point calibration, the eye-path tracking system provideda cursor indicating the participant’s eye fixation point,with the system error within 1 degree of visual angle. Asimple eye calibration was performed to verify point ofgaze before each participant was tested. The calibrationchecks were conducted prior to the presentation of thetest films. Eye movement data were subjected to aframe-by-frame analysis with a digital video recorder(60 Hz, WV-DRS, SONY Inc., Tokyo) to monitorvisual search behaviors.

The six test films of tennis serves were presentedthree times each, in random order, for a total of 18trials in the video image condition, and 18 trials in theCG animation condition. Participants were instructed toanticipate the direction (left or right side) of each tennisserve and report their decision verbally. To avoidpossible learning effects, the frames followingracket–ball contact were occluded, and no feedback wasgiven on performance in each trial. The inter-trialinterval was about 5.0 s. The order of presentation ofthe test films was counter-balanced across theparticipants such that half the participants viewed thevideo images first and the other half viewed the CGanimations first.

2.1.4. Data analysisAnticipation accuracy. This was defined as the

percentage of correct responses (PCR) in anticipatingthe direction of the opponent’s serve. The PCR scorewas subjected to a one-way analysis of variance(ANOVA) with repeated measures on one factor (filmtype). Because a PCR score of about 50% (chancelevel) would be expected if the participants were simplyguessing between the two choices, one-sample t-testswere used to determine whether the PCR score of thevideo image and CG animation conditions differedfrom chance at occlusion point.

Two measures of visual search behaviors. Percentage of viewing time. This was measured as

the duration of viewing on each viewing area in eachserve motion phase, as defined below. The serve motionphases were selected so as to provide time windowswithin the different features of the server’s movementpatterns. Previous studies (Vickers, 1992; 2007) have



shown that the minimum duration required for visualinformation extraction is 100 ms when highly practicedperformers viewed a familiar stimulus such as golfputting. On the basis of this criterion, the serve motionwas divided into four phases: Phase 1, from the readyposition to the start of the ball toss (about 690 ms timeperiod); Phase 2, from the start of the ball toss to thepoint where the ball reached its apex (about 420 mstime period); Phase 3, from the ball reaching its apex tothe completion of the backswing (240 ms time period);Phase 4, from the completion of the backswing to themoment of racket–ball contact (150 ms time period)(Figure 2 B and C). Viewing areas were classifiedobjectively by superimposing scan paths over adynamic display. The display was divided into 8viewing areas: head-shoulder, trunk-hip, arm-hand, leg-foot, racket, ball, racket–ball contact space, andunclassified (Figure 2 A). The unclassified viewingarea included blinking and any viewing outside the

areas noted above. The percentage of viewing time wassubjected to a 2�4�8 (film type�phase�area)ANOVA with repeated measures on all three factors.

Search rate. This measure included the mean numberof fixation areas per trial, mean number of fixations pertrial, and mean fixation duration. A fixation was definedas the time when the eye remained stationary(�100 ms) with 1.5 degree of movement tolerance (seeWilliams et al., 2002). The fixation areas were the sameas the viewing areas. Each dependent measure wassubjected to a one-way ANOVA with repeatedmeasures on one factor (film type).

For all statistical analyses, a probability level ofp�0.05 was considered significant. In addition, all dataexpressed as percentages were subjected to arcsinetransformation. When the assumption of sphericity wasviolated (p�0.05), the Greenhouse–Geisser methodwas applied. Significant interactions were clarified byfurther analyzing lower-level interactions and effects.



Figure 2 (A): Eight viewing areas. (B): Observed viewing time. The circle area is proportional to the percentage ofviewing time; larger circles represent longer viewing durations. (C): A schematic representation of time scale inExperiments 1 (four motion phases) and 2 (four occlusion points).

Post-hoc tests were computed according to theBonferroni test.

2.2. Results and Discussion

2.2.1. Anticipation accuracyThe PCR score was greater than chance (50%) for

both video image (87.7�11.8%) and CG animationconditions (62.3�17.0%). These data indicated that theparticipants were able to extract the perceptualinformation from the video images and CG animationsto anticipate serve direction.

A one-way ANOVA revealed that the PCR score forthe video image condition was significantly higher thanthat for the CG animation condition, F(1, 17)�26.68,p�0.01. This indicated that more accurate decisionswere made in response to the video images. However,the PCR score for the video image condition (87.7%)was higher compared to the values (75–79%) achievedin previous studies of tennis serves in temporalocclusion experiments that used video images (Farrowet al., 2005; Goulet et al., 1989). These inconsistentfindings may relate to the differences in experimenttasks between this study and the previous studies. Thisissue is addressed in greater detail in Experiment 2.

In contrast with the results of the video imagecondition, the PCR score for the CG animationcondition (62.3%) was lower than the value (75–79%)of the previous findings in the video images. Wesuggest that it was more difficult for the participants toanticipate serve direction for the CG animationcondition than for the video image conditions.

2.2.2. Visual search behaviorsSearch rate. There were no significant differences in

film type of the mean number of fixation areas, meannumber of fixations, and mean fixation duration (seeTable 1). Previous studies in sports perception havereported that the difference in the observer’s search raterelated to the film types or skill levels (Ripoll et al.,1995; Savelsbergh et al., 2002; Ward et al., 2002). Forexample, Ward et al. (2002) demonstrated that bothskilled and less skilled players used fewer fixationswith a longer duration when viewing point-lightdisplays than when looking at video images. However,our results showed no differences in the participant’ssearch rate based on film type.

Percentage of viewing time. Analysis of the visualsearch behaviors showed that there were significantmain effects of phase, F(1.55, 26.27)�33.28, e �0.52,

p�0.01, and area, F(2.26, 38.38)�53.03, e �0.32,p�0.01, and that these two factors interacted, F(3, 51)�55.67, p�0.01. Post-hoc pair-wise comparison showed that, in Phase 1, the participants spentsignificantly more time viewing the head-shoulder areathan to the other viewing areas (all p�0.01). In Phase2, they spent more time viewing the arm-hand andracket–ball contact space than the trunk-hip, leg-foot,racket, ball, and unclassified areas (all p�0.01). InPhase 3, they spent significantly more time viewing theracket–ball contact space area than the other viewingareas (all p�0.01). In Phase 4, they spent a longerperiod of time viewing the racket and the racket–ballcontact space areas than the trunk-hip, leg-foot, ball,and unclassified areas (all p�0.01). These resultssuggest that the viewing behavior for both video imageand CG animation conditions was initially placedaround the head-shoulder and arm-hand areas of theserver, but subsequently shifted to the racket–ballcontact space, or around the racket (see Figures 2 and3). This finding is supported by previous studies ontennis serves (Goulet et al., 1989; Singer et al., 1998).

Although film type had no significant effect on theoverall distribution of viewing behavior (the maineffect of film type, interactions between film type andphase, and film type and area were all nonsignificant),there was a significant three-way interaction amongfilm type, phase, and area, F(21, 357)�2.15, p�0.01.Subsequent simple interaction tests of film type by areaconducted within individual phases revealed asignificant effect in Phase 4, F(3.99, 67.77)�4.50,e �0.57, p�0.01. Tests of simple main effects showedthat participants spent a significantly longer timeviewing the racket area during Phase 4 in the videoimage condition than in the CG animation condition,F(1,17)�21.11, p�0.01.

Goulet et al. (1989) reported that skilled tennisplayers tended to shift their fixation on the racket areaduring the opponent server’s execution phase (334 mstime period immediately before racket–ball contact),



Table 1 The number of fixation locations, number of fixationsand fixation duration for the video images and CG animations(M�SD).

Video images CG animations

Number of fixation locations 2.8�0.2 2.7�0.3Number of fixations 3.2�0.3 3.2�0.4Fixation duration (ms) 402.5�48.0 426.7�87.0

which was comparable to the time amount of Phases 3and 4 in this study. Phase 4 included the racket swingmotion from the completion of the back swing until themoment of racket–ball contact. Gould (1973) indicatedthat the viewing time denotes the importance of thedisplay area being fixated. Our results suggested thatthe racket area of the tennis server during Phase 4 wasa more important viewing area in the video imagecondition than in the CG animation condition.

In summary, the CG animation condition had loweranticipation accuracy than the video image condition.In addition, we observed a difference between the twofilm types in the time spent viewing the racket in Phase4. This result can support our hypothesis that the visualsearch behaviors differed between the two film types,especially toward the racket area in Phase 4. Moreover,on the basis of the result of viewing behaviors, there isa possibility that the low anticipation accuracy in theCG animation condition was due to the differencesbetween the two film types as they related to theparticipant’s perceptual process during the 150 ms time

period immediately before contact. In Experiment 2, weassessed the information pick-up patterns across thetwo film types using a temporal occlusion method (e.g.,Abernethy et al., 2001; Farrow et al., 2005; Goulet etal., 1989).

3. Experiment 2

3.1. Methods

3.1.1. Participants Ten skilled (M�SD: age, 20.9�1.79 years) tennis

players were recruited as new participants forExperiment 2. The skilled group in Experiment 1 didnot participate in this experiment because of graduationor other activities. Thus, we recruited 10 skilled playersfrom the same college tennis club used for Experiment1. They had a mean of 7.7 (SD�2.8 years) playingexperience, and had played a mean of 231 (SD�79)tournament matches. All participants provided theirinformed consent prior to the experiment, and had



Figure 3 The means and standard deviations of the percentage of viewing time at each viewing area for the videoimages and CG animations across the four phases.

normal visual acuity.

3.1.2. Test filmsThe 12 test films (six for video images and six for

CG animations) from Experiment 1 were edited using atemporal occlusion method. The serve motions wereoccluded at four different points, one at the end of thefour phases described in Experiment 1: t1, the start ofthe ball toss (about 810 ms before the moment ofracket–ball contact); t2, the ball reaching its apex(about 390 ms before the contact); t3, completion of thebackswing (150 ms before the contact); t4, the momentof racket–ball contact (Figure 2). In total, there were48 test films, consisting of six serves (three left andthree right), at each of four occlusion points (t1, t2, t3,and t4) for each film type (video images and CGanimations).

3.1.3. Procedure The set-up in this experiment was the same as in

Experiment 1 except that there was no eye-movementrecording in this experiment. The 24 test films (sixserves�four occlusion points) for each film type werepresented twice each in random order, providing a totalof 48 trials for the video images and 48 trials for theCG animations. There was an inter-trial interval ofabout 5.0 s. The presentation order of film type wascounterbalanced across participants. The participantswere instructed to anticipate the direction (left or rightside) of the given tennis serve and to verbally announcetheir decision. To avoid learning effects, theparticipants did not receive feedback.

3.1.4. Data analysisAs in Experiment 1, anticipation accuracy was

measured as the PCR. The PCR score was subjected toa 2�4 (film type�occlusion) ANOVA with repeatedmeasures on both factors. A one-sample t-test was usedto determine whether the PCR score of the videoimages and CG animations in each occlusion point (t1,t2, t3, t4) differed from 50% (chance level). For allstatistical analyses, a probability level of p�0.05 wasconsidered to be significant. Post-hoc tests werecomputed according to the Bonferroni test.

3.2. Results and Discussion

The PCR score (Figure 4) showed no significantmain effect of film type. However, there was asignificant main effect of occlusion, F(3, 27)�10.27,

p�0.01, and a significant interaction between film typeand occlusion, F(3, 27)�7.35, p�0.05. Post-hoc pair-wise comparison revealed that, for the video imagecondition, the PCR score at the t4 occlusion point washigher than that at each of the other three occlusionpoints (all p�0.01). In contrast, for the CG animationcondition, no significant differences were seen betweenany of the four occlusion points. These results suggestthat, for the video image condition, the participantswere able to pick up information during the t3–t4 timeperiod that helped them to significantly improve theiranticipatory accuracy for serve direction. However, forthe CG animation condition, improvement in accuracywas small across the same time period.

Of note, although accuracy for both the video imageand CG animation conditions was superior to chanceonly at the t4 occlusion point, the PCR score for thevideo image condition was significantly higher thanthat for the CG animation condition at t4, F(1, 9)�6.34, p�0.05. The PCR score at t4 was veryclose to the values (75–79%) obtained using videoimages in previous studies (Farrow et al., 2005; Gouletet al., 1989). However, although we used the samevideo images in both Experiments 1 and 2, the PCRscore of the video image condition at the racket–ballcontact was lower in Experiment 2 (75%) than inExperiment 1 (87%). In Experiment 1, the participantswere able to predict the occlusion timing of test filmsbecause the length of all test films was the same. Incontrast, in Experiment 2, the participants were not ableto predict the occlusion timing of test films because thetest films consisted of four different occlusionconditions. The differences in the presentation methods



Figure 4 The means and standard deviations of anticipationaccuracy for the video image and CG animation conditions for eachocclusion point. ** p�0.01 * p�0.05. † Significantly above chance levels of 50% (p�0.01).

of the test films may have made Experiment 2 moredifficult than Experiment 1.

In contrast with the results of the video imagecondition, for the CG animation condition, the PCRscore (60%) at t4 was lower than the values in theprevious studies (Farrow et al., 2005; Goulet et al.,1989). These results are consistent with those ofExperiment 1. It is suggested that the participants wereable to extract the more useful anticipatory informationfrom the t3–t4 time period in the video image conditionthan in the CG animation condition.

4. General Discussion

This study investigated the visual search behaviors(Experiment 1) and information pick-up patterns(Experiment 2) of skilled tennis players while theyviewed and verbally judged the anticipated servedirection from either video images or CG animations.In Experiment 1, the participants spent more viewingthe racket area in the video image condition than in theCG animation condition during the 150 ms periodimmediately before contact (Phase 4). In addition, theparticipants showed more accurate performance withthe video image condition. In Experiment 2, for thevideo image condition, the participants were able tomore accurately anticipate the direction of theiropponent’s serve using the information available in the150 ms period immediately before contact (t3–t4 timeperiod) than was the case in the CG animationcondition. These results showed that there weredifferences in the participant’s perceptual processesbetween the two film types in the period immediatelybefore contact.

In the 150 ms period immediately before contact, theracket segment is maximally involved in the hittingaction, and a sequence of rotational movements ofracket–arm segments contribute to the racket headspeed (Sprigings et al., 1994). Skilled tennis playersneed to pay attention to the relationship between themovements of the arm-hand, racket, and toss-up ball inorder to determine the serve direction of an opponent(Goulet et al., 1989; Singer et al., 1998; Jackson andMogan, 2007). Our results suggested that the racketswing motion in the video image condition provided aparticularly rich source of information to guide theparticipant’s anticipatory performance when comparedto the CG animation condition.

Previous studies on tennis serves reported that theaccuracy of skilled tennis players was about 80% of the

trial at the racket–ball contact, and that value was overthe chance level of 50% (Farrow et al., 2005; Goulet etal., 1989). However, other previous studies in fieldsettings demonstrated that the accuracy of skilled tennisplayers was about 60%, that value was just over thechance level of 50% (Farrow and Abernethy, 2003).Also, the accuracy of expert tennis coaches was about40% which was marginally better than the 33.3%expected by chance (Jones and Miles, 1978). Theseinconsistent results could relate to the occlusion timingat the racket–ball contact. In this study, thecharacteristic data (motion data, size, and colorinformation) of the racket and ball in a real situationwas reproduced in the CG model. However, the slightlyaltered shape of the ball at the moment of racket–ballcontact in a real situation was not. This is because theracket and ball in the CG model each had a rigid body,which is not altered in shape unless the CG creatormodifies it. It is thought that the altered shape of theball in the video images included some slight visualinformation relating to the ball-flight. This may haveenhanced the participant’s anticipatory performanceduring the 150 ms period immediately before contact inthe video image condition.

In addition, the CG human model (except for theracket and ball) captured only the motion data from thereal situation because the CG human model was anexisting polygon model in the CG software. Thus, therewere differences between the human model in the CGanimations and the real human in the video imagesregarding the size and figuration information: (i) thesize of the cross-section as area of each human bodysegment in a real situation was not reproduced in theCG human model. (ii) when the arms or legs in the CGhuman model moved in a certain direction, the skin inthe regions where the joints bend was folded,compressed, or bulged unnaturally. (iii) the CG humanmodel was a nude model that did not wear sportsclothing, shoes, or a cap. (iv) the lighting angle in theCG animations did not adjust to the lighting in thevideo images. All of these differences between the twofilm types may have led to difficulty in anticipatoryjudgments during the 150 ms period immediatelybefore contact in the CG animation condition.

However, importantly, the anticipation accuracy ofthe CG animation condition in both Experiments 1 and2 was superior to the chance level (50%). Thisindicated that the participants were able to extract theperceptual information from our CG animations toanticipate serve directions. The CG animations offer, at



least partly, the possibility that they can be used forevaluating perceptual skills in tennis. Bideau et al.(2003;2004) examined handball goalkeepers’ responseswhen faced with an opponent’s throw in a VRenvironment as compared to a real situation. The studydemonstrated that the VR situation was realistic enoughthat it made it possible for researchers to evaluate thegoalkeeper’s natural reactions. To evaluate perceptualskills in tennis using CG animations in the future, it isnecessary to examine the reason for enhancedanticipation accuracy in the video image condition.Moreover, if CG animations can be used to evaluate theexperience or skill level of athletes, this technology willbe a valuable tool in sports perception studies.

There are some limitations to this study. The testfilms showed six serve types, and we recruited only onemodel server. In the future, the use of additional servemotions and model servers may lead to a greaterunderstanding of the perceptual information in CGanimations.

AcknowledgementsThis research was supported by a Grant-in-Aid for Scientific

Research ((B) 19300218) from the Japan Society for the Promotionof Science (JSPS).

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Address: 2-12-1-W9-601 O-okayama, Meguro, Tokyo 152-8552 JapanBrief Biographical History: 2002- Master’s Program, Department of Human System Science,Tokyo Institute of Technology2004- Doctoral Program, Department of Human System Science,Tokyo Institute of TechnologyMembership in Learned Societies:• Japanese Society of Sport Psychology• North American Society for the Psychology of Sport and Physical

Activity

Name: Kazunobu Fukuhara

Affiliation: Doctoral Programs, Department of Human System Science, Tokyo Institute ofTechnology

Documents

Anticipatory Judgment of Tennis Serve: A Comparison