precompetition emotions & shooting performance

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The Sport Psychologist 1991, 5, 223-234

Precompetitive Emotions and ShootingPerformance: The Mental Health and Zone

of Optimal Function Models

Harry Prapavessis and J Robert Grove

The University of Western Australia

This smdy tested the utility of Morgan's (1980) Mental Health Model and

Hanin's (1980) Zone of Optimal Function Model in an ecologically valid

environment. A sample of 12 high-performance adult clay-target shooters

were tested over an entire competitive season. Precompetitive mood states

were assessed using Schacham's (1983) short version of the Profile of Mood

States (POM S; M cNa ir, Lorr, Droppleman, 1971). Results revealed partial

support for Hanin's model but no support for Morgan's model. Discussion

focuses on the importance of m ultiple assessments of precom petitive emotions,

recognition of individual differences, and selection of a precise measure ofsport performance.

The ability to produee and maintain the appropriate emotional feelings beforecompetition is universally recognized by athletes and coaehes as one of the mostimportant factors contributing to athletic performance. Thus it is not surprisingthat the relationship between precompetitive emotions and sport performance hasgenerated considerable interest from researchers in the field of sport psychology(Gould, Horn, Spreemann, 1983; Gould, W eiss, W einberg, 1981; Highlen Bennett, 1979; Mahoney Av ener, 1977; M eye rs, Coo ke, CuUen, Liles,

1979; Mo rgan, 1979, 1980; Morgan Johnso n, 1978; Mo rgan Pollack, 1977;Silva, Schultz, Ha slam , M artin, M urray , 1985; Silva, Schultz, Haslam ,Murray, 1981).

From a methodological perspective, previous r^earch has often focusedon discriminating between successfiil and unsuccessful performers based on theirmood states prior to competition. The Profile of Mood States (POM S) developedby McNair, Lorr, and Droppleman (1971) has been a popular instrument formeasuring athletes' affective pattems prior to competition. Sport performancehas usually been measured as the placement that athletes achieve in competition



2 2 4 • P r a p a ves s i s a n d r o ve

relative to their competitors. This placement or ranking has then been used tocategorize athletes into successful or unsuccessful groups (i.e., top 10 vs. top5 0; qualifiers vs. nonqualifiers).

Results from these studies suggest that successful competitors evidence amo re positive psychological profile than less successful com petitors. M ore specifi-cally, it appears that superior performance by elite athletes is reflected in lowscores in tension, depression, anger, fatigue, and confusion and high scores invigor during the precotnpetitive period. This positive mental health profile hasbeen termed the iceb erg pr ofi le by M organ (1979 ), since the five negativeemotions fall below the 50th percentile and the one positive emotion lies above it.

Unfortunately, the relationship between precompetitive emotions and sportperformance presented by Morgan's Mental Health Model suffers from severalconceptual, m ethodological, and interpretive pro blem s. Fo r instance, none of the

studies mentioned above has assessed athletes' mood states more than once duringa competitive season to determine whether the Mental Health Model can consis-tently explain and predict performance. In addition, these studies have exclusivelyused between-subject comparisons rather than focusing on intraindividual varia-tions, and they have ignored the possibility that precompetitive m ood state profilesrelated to successful and unsuccessful performances may vary across individuals.

Finally, there has also been a lack of precision in the manner that perfor-mance has been operatiotially defmed in previous studies. This has created inter-pretive problems regarding the precompetitive mood state/performancerelationship. For instance, some studies have shown that qualifiers for national

squads have different mood state profiles at the precompetitive stage of the sportcontest than do nonqualifiers (e.g ., Silva e ta l. , 1981; Silva et a l. , 1985 ). Thesefindings have been interpreted to mean that the profile produced the good perfor-mances.

This interpretation is confounded, however, by the fact that precompetitivestatus could just as easily produce the mood state profile. Highly ranked athleteswho are assured selection are not likely to be overly stressed during qualifyingrounds, and as a result they should demonstrate relatively positive precompetitiveemo tions. On the other hand, b orderline athletes are m ore likely to perceive pres-sure and a re also less likely to be selected. T hu s, athlete status could determ inemood profiles during the precompetitive period rather than mood profiles determin-ing performance and status.

Support for this proposition is provided from research reported by Heyman(1982). He reanalyzed a series of articles comparing the psychological profileof successful and unsuccessful athletes and found that the successftil ones hadsignificantly better season record s, m ore experience, and came from superior train-ing pro gram s. Th us, events preceding the psychological testing and performaticecriteria appear to have influenced the observed differences. The importance ofhow sport performance is measured in precompetitive emotion research also isillustrated in a study by Ebbeck and Weiss (1988), who found that A-state wasnot equally related to various performance nieasures (i.e., event results vs. thesubjective evaluation of performance by coach and athlete). Their findings suggest



Precompetitive Emotions and Performance • 5

has been proposed by Hanin (1980, 1986, 1989). According to Hanin, eachindividual has an arousal zone of optimal function (ZOF), and performanceefficiency is best when the level of arousal falls within this zon e. In othe r w ord s,the ZOF theory predicts that some individuals will have their best performance

when highly aroused, others when deeply relaxed, and others when moderatelyaroused. Hanin's empirical evidence for this theory is based on the testing ofathletes over many competitive trials and thereby demonstrating the validity ofthe ZOF (Hanin, 1980, 1986).

Empirically, Hanin found a correlation of .74 between successful athleticperformanc e and the degree to which each athlete was able to achieve his or h eroptimal arousal as measured by the State-Trait Anxiety Inventory (STAI; Spiel-berger, 1972). Although the ZOF principle has not yet been extended to otheremotions (e.g., anger, vigor, depression), it seems reasonable to suggest that theremay be an individualistic ZOF for mood states other than arousal/anxiety.

Conceptual support for Hanin's ZOF principle can be found in recentreviews on the relationship between arousal/anxiety and motor performance(Landers, 1980; Mahoney M eyers, 1989; Neiss, 1988; W einberg, 1990). Theseresearchers arg ue that individual differences appear to mediate optimal arousal/anxiety levels. They recommend using intraindividual measures that compare anathlete's usual level of arousal/anxiety with changes from this level, and thencom paring the impact of these changes on sport performan ce. T he im portance ofintraindividual analysis when investigating the arousal/anxiety-motor performancerelationship has also been demon strated by Burton (198 8), Go uld, Petlichkoff,Simons, and Vevera (1987), Klavora (1978), and Sonstroem and Bernardo (1982).

Adopting a multiple assessment approach, recognizing individual differ-ences, and providing precise measurement of sport performance are some of therecommendations that Weinberg (1990) made in a recent review of the relation-ship between anxiety and motor performance. From a more global perspective.Martens (1987) siniilarly recommended that the study of athletic behavior beviewed within a heuristic paradigm that emphasizes an idiographic approach (i.e.,the in-depth study of the individual performer).

The purpose of the present study w as to test the utility of M org an 's Men talHealth M odel and Han in's Z O F Model in an ecologically valid environmen t. T he

sport of shooting was chosen because it represented a closed skill and perfor-mance could be assessed precisely. It was hypothesized that precompetitive moodstates would be related to shooting performance but that these mood states wouldbe specific to each individual in a manner consistent with Ha nin 's Z O F principle.Although no previous sport research directly bears on this issue, it does seempossible that Ha nin's (198 0, 1986, 1989) findings on anxiety and athletic pe rfor-mance could be extended to other emotions.

etho

Subjects

A sample of 11 male and 1 female high-performance clay-target sho oters took



2 26 Pr apaves s i s a n d G r o v e

and consent form agreeing to take p art in the study. Subjects were assured thattheir results would be kept in the strictest confidence.

Procedure

Initially, the state coaching director and state squad m emb ers w ere contacted to

determ ine their interest and request their cooperation. A m eeting w as then arrangedto explain and outline the research project, and a testing schedule was organizedaround the impo rtant co mpetitions in each shoo ter's competitive season. E achshooter subsequently filled out a mood state inventory ap proximately 10 minutesbefore each competition throughout the 12-month period. Shooters participatedin six to nine major tournaments throughout the 12-month period, liie researchproject was part of a mental skills training program that was developed and im-

plemented by the first author, who was present at approximately 75 of the com-

petitions to

ensure that subjects completed the

mood state inventory at the

appropriate time.

ood State Scale

Schacham's (1983) shortened version of the P rofile of Mo od States (POM S) was

used to assess precompetitive mood states. The scale consists of 37 items thatcontribute to subscales for tension, depression, fatigue, anger, vigor, and confusion.Schacham found that the correlation coefficients between the shortened and originalscales were all above .95 , indicating the suitability of the short version for es-

timating the original mood scale scores. The m ood state subscales and the specific

items related to them are presented in Tab le 1. In order to prevent subjects fromdeveloping a particular response set, several versions of the mood state inventorywere randomly distributed to the shoo ters. Subjects w ere asked to respond in termsof how they felt rig ht now.

P e r f o r m a n c e M e a s u r e s

Each competition consisted of 100 targets which w ere shot in four separate roundsof 2 5 . Sub jects' performance scores over the com petitive season w ere subdivided

Table Subscales and Items for the Shortened POMS Schach am, 1983)

Subscaie Item

Fatigue Worn out bushed fatigued exhausted weary

Anger Peeve d bitter resentful grouchy angry furious annoyed

Vigor Cheerful vigorous full o pep. active energ etic lively




into one of three categories: optim al, acceptable, and wo rst. The optimal perfor-mance category represented each s hoo ter s two best scores for the year. The

acceptable performance category represented each shooter s midrange scores. Theworst performance category represented each shooter s two lowest performance

scores for the year.

sults

Performance C ategories

Preliminary analyses were conducted to verify that performance scores differedacross the three performance categories. Results of a one-way repeated measuresANOVA indicated that there were significant differences among categories,F ( 2 , 2 2 ) = 4 0 . 2 2 , / K . 0 0 1 . Follow-up comparisons using Tu key s HSD procedurerevealed that shooting scores for the optimal category (A/=83.75) differed sig-

nificantly p<.0\) from the acceptable category ( A /=7 8.6 6). T he accep tablecategory also differed significantly p<.01) from the worst category (A/=72.58).

Hanin s ZOF Model

In order to test the utility of Ha nin s Z O F M odel, the following comp utationalproce dure was employed. Scores were calculated for the average level of givenmood prior to optimal, acceptable, and worst performance for all subjects. Twodifference scores were then obtained for each mood-state variable: (a) that betweenthe level of the mood during optimal and acceptable performances, and (b) that

between the level of the mood during optimal and w orst performances. SinceH an in s ZO F M odel focuses on the amount rather than the direction of these dif-ference scor es, they w ere treated in terms of their absolute values in the analysis.

H an in s theory w ould be supported if the absolute difference scores betweenoptimal and acceptable performances yielded smaller values than the absolutedifference scores between optimal and worst performances. One-way ANOVAswere used to compare difference scores (optimal minus acceptable vs. optimalminus worst) for each of the individual mood state subscales .

A total mood disturbance (TMD ) score was also calculated to obtain a singleglobal estimate of mood state. TMD was calculated by adding the five negative

mood states (tension, depression , fatigue, anger, and confusion) and then subtract-ing the one positive mood state (vigor). According to McNair et al. (1971), theTMD score is clinically re levan t, and it can be presumed to be highly reliablebecause of the intercorrelations among the six primary POM S factors.

The validity of TMD has been demonstrated in a number of sport studiesusing the POMS (e.g ., M eyer, Sterling, LeU nes, 1988; M organ, Brow n,Raglin, O Con nor, Ellickson, 1987; Nation LeUnes, 1983; Robinson How e, 1987). With the TM D score as the dependent measure, a one-way ANOV Awas used to detennin e w hether mood state variations w ere greater across optimaland worst shoots than across optimal and average ones. Table 2 shows absolutevalues for the differences obtained between optimal and acceptable shoots as wellas those between optimal and worst shoots for T MD and the six POM S subscales.



228 • Prapavessis and Grove

Table 2

Absolute Values of Difference Scores for Precompetitive Mood States

Variables

Tension

A n g e r

Fatigue'

Vigor

Depress ion

Con fus ionT M D ' * '

Optimal vs . acceptable performances Optima l vs.

2.40

1.65

2.59

2.72

1.54

0.877.56

SD

1.42

2.53

1.58

1.92

1.49

1.535.45

2.59

3 18

4 30

3 50

3 69

2 1916 55

worst performances

S

1.57

3 35

3 87

3 65

3 66

2 4613.01

'TM D = total mood disturbance;

ferences w ere found for the subscales of tension, F ( l , 11 = 0 . 0 9 , p>.10; or vigor,F l,l 1 ) = 0 . 6 0 , p>.40. Resu lts of the TM D analysis revealed a significant differ-ence in mood state variation for op tim al minus ac cep tab le performan ces as

compared to optimal minus w orst performances, F ( l , l l ) = 7 .6 6 , p<.02. Aspredic ted, scores we re greater for the op tim al minus w o rs t condition than forthe op tim al minus ac ce pta ble condition for all variables in which differenceswe re obtained (see Table 2) . A graphic representation of these results is presentedin Figure 1.

Morgan s Mental H ealth Model

order to test the utility of Morgan's Mental Health Model, raw mood statescores were com pared across the different performance categories (optima l, accept-able, and worst). One-way ANOVAs were conducted for TMD and each of the

six subscales in order to make these comparisons. TMD was calculated by ad-ding the five negative mood states (tension, depression, fatigue, anger, and con-fusion) and then subtracting the one positive mood state (vigor). Evidencesupporting this model would be obtained if generalized differences in mood statescores were found across performances of varying quality.

Mean values of the mood state variables for each performance categoryare presented in Table 3. Results of the AN OV As revealed no significant dif-ferences in TMD across optimal, acceptable, and worst performances,F ( 2 , 2 2 ) = 0 . 0 2 , p<.9S. Similar results were obtained for each of the six PO MSsubscales when they we re examined (all Frat ios < 1 , all/? values >.4 5) . A graphicrepresentation of these results is presented jn Figure 2.



230 • Pra p a vess i s a n d ro ve

Table 3

Raw Precom petitive M ood State Scores

for Optimal Acceptable and Worst Performances

Variables

Tension

Anger

Fatigue

Vigor

Depression

Confusion

TMD

Optimal

M

8.60

2.68

2.90

9.15

2.90

2.36

10.36

3.03

3.34

1.94

5.17

3.92

2.45

3.90

Acceptable

M

8.76

2.77

2.61

8.23

2.16

1.54

9.62

S

4.23

4.75

3.03

5.41

3.16

1.76

5.35

Worst

M

7.95

2.33

3.70

8.45

2.45

2.41

10.41

4.18

4.73

4.94

5.34

4.34

2.76

7.88

= total mood disturbance;

Note None of the group differences are significant.

and that the relationship between precompetitive mood states and sport perfor-mance is more complex than is acknowledged by M org an s m ode l. To simply

average across subjects ignores the possibility that a score of 5 on a particular m oodsubscale may be a high score for one athlete but a low score for another. Thus,similar scores could produce performance differences for different individuals.In other wo rds , an athlete s specific reaction and pattern of emotional changemay be more important than the absolute level of the emotion in determiningperformance (Weinberg, 1990).

Another possible reason for failure to support the utility of Morga n s theorymay be the different way that sport performance was operationally defined inthe present study. For example, previous studies have shown that qualifiers fornational squads have different mood profiles before competition than do non-

qualifiers. These findings have often been interpreted to mean that the profileproduced the good performance. However, this interpretation is confounded bythe fact that precompetitive status could just as easily have produced the moodprofile. Athletes who were assured selection w ere likely to demonstrate positiveprecompetitive mood states whereas those who were borderline were likely tohave been m ore anxious, tense , confused, and so forth. Th us, status may determinethe mood state profile before competition rather than the mood state profile deter-mining performance and status.

Heyman s (1982) research comparing successful and unsuccessftil competi-tors provides general support for this proposition. His fmdings suggest that

previous success needs to be considered along with preperformance psychologicalassessment when predicting contest outcome. In the present study, sport perfor-



Precompetitive Emotions and Perfbnnance • 231

0

0)Q.

ou0)

10

8 -

6 -

4 -

2 -

•o- op t ima l

-•- Acceptable

-O- Wors l

TEN ANG FAT ViG O f f CCN

Mood State Subscales

Figure 2 — Raw scores for mood state ratings obtained from shooters during theprecompetitive period. ;

mood states related to shooting performance would be specific to each individualin a manner consistent with Hanin's Zone of Optimal Function Model (Hanin,1980, 1986, 1989). Support for the utility of Hanin's theory was clearly demon-strated by the finding that mood state difference scores across optimal and accept-able performances consistently yielded smaller values than difference scores acrossoptimal and worst performances. TMD and all six mood state subscales conformedto this pattem , w ith significant differences evident in four of these measures ( i.e .,anger, depression, confusion, and TMD).

These findings are consistent with the suggestion that individual differencesneed to be considered when investigating the relationship between emotions and

sport performance (Landers, 1980; Maboney M eye rs, 1989; M artens , 1987;Ne iss, 1988; W einberg, 1990). The present findings also support the suggestion



2 32 • P ra p a vess i s a n d G ro ve

A number of important implications can be drawn for the coach and practic-ing sport psychologist. For instance, since precompetitive mood states relatedto optimal performance were found to vary across individuals, it would seemprudent to ascertain what can be done to help athletes develop and maintain their

ideal performance mood state prior to competition. It is recommended that pre-competitive mood state levels particular to each athlete be measured over a seriesof comp etitions. Th is would help identify the specific mood states that are condu-cive for optimal performance to consistently occur.

Once this is done, specific self-regulation strategies such as biofeedback(Daniels Lan ders, 1981; DeW itt, 1980), coping skills (Meichenb aum, 1977),psyching-up (Weinbe rg, Gou ld, Jackson, 1980), and/or mental imagery (Feltz Lan ders, 1983) could be introduced to alter debilitative precom petitive moodstates and to facilitate productive mood states. The findings of the present studyargue against the use of group oriented assessment as well as the use of grouporiented intervention procedures because they do not consider individualdifferences.

Although we have argued that these data support the utility of H anin 's ZO FModel, the findings must be interpreted with certain methodological limitationsin mind. Pertiaps the most important of these limitations is the use of multipleunivariate analyses and the corresponding risk of Type I error . If a more conserva-tive alpha had been employed to compen sate for m ultiple AN OV As , some of theeffects noted in Table 2 may not have been statistically significant.

W e acknow ledge the possibility that one or more of these differences could

be due to chance , but we believe there is evidence for the utility of the ZOF modeldespite this potential problem. To begin with, two-tailed (.05) tests were usedto examine d i r e c t i o n a l hypotheses which could have been justifiably evaluatedusing one-tailed (. 10) tests. In add ition, all of the significant differences occurredfor the Hanin model rather than the Morgan model. Tliis pattern of results suggeststhat the differences were systematic rather than random.

Finally, the difference scores calculated from the three performancecategories were consistently in the direction predicted by the ZOF Model. Thischaracteristic of the data is apparent in both Table 2 and Figure 1, which showthat all seven mood state variables produced scores that were in the expected

direction. Nevertheless, at this stage it may be prudent to interpret these differencesas consistent trend s rathe r than significant effects.

The small sample size represents a second methodological limitation in thepresent study. Certainly replicative research using an in-depth idiograjAic approachwith larger numbers of subjects is needed to provide more definitive support ofHa nin's ZO F M odel. A particularly interesting direction for this research appearsto be the extension of Hanin's ZOF principle to emotions other than anxiety.Studies along these lines could prov ide a num ber of practical benefits for coaches,athletes, and sport psychologists.




DeW itt, D. J. (1980). Cognitive and biofeedback training for stress reductions with un ive rsi^

athletes. Journal of Sport Psychology, 2, 288-294.

Ebbeck, V ., Weiss, M.R. (1988). The arousal-performance relationship: Task character-

istics and performance measures in track and field athletes. The Sport Psychologist,

2, 13-27.

Feltz, D. L ., Landers, D.M . (1983). The effects of mental practice on motor skill learning

and performance: A meta-analysis. Journal of Sport Psychology, 5, 25-87.

Gould, D ., Hom , T ., Spreemann, J. (1983). Competitive anxiety n junior elite wrestlers.

Journal of Sport Psychology, 5, 58-71.

Gould, D. , Petlichkoff L., Simons, J., Vevera. M. (1987). Relationship between

Competitive State Anxiety Inventory-2 subscaie scores and pistol shooting perfor-

mance. Journal of Sport Psychology, 9, 33-42.

Gou ld, D ., W eiss, M ., W einberg, R. (1981). Psychological characteristics of successful

and nonsuccessful Big Ten wrestlers. Journal of Sport Psychology, 3, 69-81.Hanin, Y .L . (1980). A study of anxiety in sports. In W .F . Straub (Ed .), S port psychology:

An analysis of athlete behavior (pp. 236-249). Ithaca, NY; Mouvement.

Han in, Y. L . (1986 ). State-trait anxiety research on sports in the USSR . In C D . Spielberger

R. Dias-Guerrero (Eds .), Cross-culture anxiety (pp. 45-64). Washington, DC:

Hemisphere.

Hanin, Y .L . (1989). Interptrsonal and intragroup anxiety: Conceptual and methodological

issues. In C D . Spielberger D . Hackfori (Eds.), Anxiety in sports: An intemationai

perspective (pp. 19-28). Washington, DC: Hemisphere.

Heyman, S.R. (1982). Comparisons of successful and unsuccessful competitors: A re-

consideration of methodological questions and data. Journal of Sport Psychology,4, 295-300.

High len, P. S. , Bennett, B.B . (1979). Psychological characteristics of successful and

nonsuccessful elite wrestlers: An exploratory study. Journal of Sport Psychology,

1, 123-137.

Klavora, P. (1978). An attempt to derive inverted U curves based on the relationship between

anxiety and athletic performance . In D .M . Land ers R.W . Christina (E ds .), Psy-

chology of motor behavior and sport {p^. 369-377). Champaign, IL: Human Kinetics.

Land ers, D .M . (1980). The arousal-perfonnance relationship revisited. Research Quarterly

for Exercise and Sport, 51, 77-90.

M ahone y, M .J., Ave ner, M. (1977). Psychology of the elite athlete: An exploratorystudy. Cognitive Therapy and Research, 1, 135-141.

Maho ney, M .J., Me yers, A.W . (1989). Anxiety and athletic performance: Traditional

and cognitive-developmental perspectives. In D . Hackfort C D . Spielberger (Eds.),

Anxiety in sports: An intemationai perspective (pp. 77-94). Washington, DC:

Hemisphere.

Martens, R. (1987). Science, knowledge, and sport psychology. The Sport P sychologist,

1. 29-55.

McNair, D .M ., Lorr, M ., Droppleman, L.F . (1971). Profile of mood states manual.

San Diego: Educational and Industrial Testing Services.

Meichenbaum, D. (1977). ognitive behavior mo dification: An integrative approach. New

York: Plenum.



234 • Prapavessis and Grove

Mo rgan, W. P. (1979). Prediction of performance in athletics. In P. Klavora J .V . Daniel

(Eds.), Coach, athlete, and the sport psychologist (pp. 173-186). Champaign, IL:

Human Kinetics.

Morgan, W.P. (1980). The trait psychology controversy. Research Quarterly for Exercise

and Sport, 51, 50-76.

Morgan, W .P., Brown, D.R ., Raglin, J.S . , O'Co nnor, P.J. , Ellickson, K.A. (1987).

Psychological monitoring of overtraining and staleness. British Joumal of Sports

Medicine, 21, 107-114.

M organ , W .P ., Johnson , R. (1978 ). Personality characteristics of successfiil and un-

successful oarsmen. International Joumal of Sport Psychology. 9, 119-133.

M organ , W .P ., Pollock, M . (1977). Psychological characteristics of the elite distance

runner. Annals of the New York Academy of Science, 301, 382-403.

Nation, J.R ., LeU nes, A . (1983). Personality characteristics of intercollegiate players

as determined by position, classification, and redshirt status. Joum al of Sport Be-havior. 6, 92-102.

Neiss, R. (1988). Reconceptualizing arousal: Psychological states in motor performance.

Psychological Bulletin, 103, 345-366.

Robinson, D ., Ho we, B . (1987). Causal attribution and mood state relationships of soccer

players in a sport achievement setting. Joum al of Sport Behavior, 10, 137-146.

Schacham, S. (1983). A shortened version ofth e Profile of Mood S\2\ ^ . Jouma l of Person-

ality Assessment, 47, 305-306.

Silva, J.M ., Schultz, B. B. , Haslam, R.W ., Martin, T .. MurTay, D. (1985). Dis-

criminating characteristics of contestants at the United States Olympic wrestling

trials. International Joumal of Sport Psychology, 16, 79-102.Silva, J. M ., Schultz, B.B ., Haslam, R.W ., Mu rray, D. (1981). A psychophysioiogical

assessment of elite wrestlers. Research Quarterly for Exe rcise and Sport, SI,

348-358.

Sonstroem . R. J., Be rnard o, P. (1982 ). Individual prega me state anxiety and basketball

performance: A re-examination of the inverted-U curve . Jouma l ofSpon Psychology,

4, 235-245.

Spielberger, C D . (1972). Anxiety as an emotional state. In C D . Spielberger (Ed .), Anxiety:

Current trends in theory and research (Vol. J) (pp. 22-49). New York: Academic

Press.

Vallerand, R.J. (1984). Emotion in sport. Definitional, historical, and social psycho-

logical perspectives. In W .F . St ra ub J. M . Williams (Eds.), Cognitive sport psy-

chology (pp. 65-78). Ithaca, NY: Sport Science Associates.

Weinberg, R.S. (1990). Anxiety and motor performance: Where to from here? Anxiety

Research, 2, 227-242.

W einberg, R .S ., Gould, D ., Jackson, A. (1980). Cognition and motor performance:

Effects of psyching up strategies on three motor tasks. C ognitive Therapy and R e-

search, 4, 239-245.

Manuscript submitted February 13, 1990

Revision received February 1, 1991 >

precompetition emotions & shooting performance

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