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JOURNAL OF APPLIED SPORT PSYCHOLOGY, 16: 151–165, 2004 Copyright © Association for Advancement of Applied Sport Psychology ISSN: 1041-3200 print / 1533-1571 online DOI: 10.1080/10413200490437679

Precompetition Emotions, Bodily Symptoms, and Task-Specific Qualities as Predictors of Performance in High-Level Karate Athletes CLAUDIO ROBAZZA AND LAURA BORTOLI Università di Padova

YURI HANIN Research Institute for Olympic Sports The study, based on the Individual Zones of Optimal Functioning (IZOF) model, examined the practical utility of precompetition idiosyncratic emotions, bodily responses, and task-specific qualities (physical, technical, and tactical performance characteristics) in predicting the performance of ten Italian high-level karate athletes. First, athletes recalled their best and worst performances to develop individualized scales (profiles) with 33-idiosyncratic items (12 items for emotions, 12 for bodily responses, and nine for task-specific qualities). These scales were then used to assess the athletes’ actual emotions, bodily responses, and task-specific qualities 15 minutes prior to the first-round fight in ten competitions across the entire season. It was revealed that idiosyncratic emotions and bodily responses differentiated between successful and less than successful (average) performances. These findings provide empirical support for the validity and practical utility of the in/out-of-zone notion extended to bodily symptoms. In contrast, relatively stable, task-specific characteristics did not differentiate between individually good and average situational performances. Future directions and practical implications of the study are suggested.

One of the key factors in enhancing athletic achievement, especially in high-level sport, is an accurate, task-relevant, and individual-oriented prediction of performance based on emotional states (Annesi, 1998; Gould & Udry, 1994; Hanin, 1980, 1997; Hardy, Jones, & Gould, 1996). Such predictions may create a sound basis for the development of effective mental training programs for the athlete and team. Unfortunately, most of the existing approaches employ nomothetic models that emphasize general principles of behavior derived from the study of groups and thus are hardly applicable to individual athletes. Another limitation of these group-oriented perspectives is their predominant focus on precompetition anxiety. Finally, a

Received 10 October 2002; accepted 1 April 2003. The research was supported by a grant of Istituto Superiore di Educazione Fisica, Bologna, Italy. The authors acknowledge Elena Brunello and Franco Campanati for their help in data collection. The authors thank the editor, the section editor, and the two anonymous reviewers for their insightful comments and suggestions in an earlier version of this paper. Address correspondence to Claudio Robazza, Ph.D., Dipartimento di Scienze Mediche e Chirurgiche, Polo 40 Semeiotica Medica, Via Ospedale Civile, 105, 35128 Padova, Italy. E-mail: [email protected] 151

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group-oriented approach may underestimate the phenomenology of performance-related experiences reflecting an athlete’s perspective (Dale, 1996). Therefore, an idiographic (individualoriented) approach to the prediction of individual athletic performance holds promise, especially in the field setting of high-achievement sport. Our exploratory study used the Individual Zones of Optimal Functioning (IZOF) model (Hanin, 1997, 2000b, 2000c) as a conceptual framework and methodological tool to examine the effectiveness of individual-oriented predictions of performance in highly skilled and experienced athletes. Space limitations preclude a detailed review of extant literature featuring the IZOF model and relevant empirical research. Therefore, readers are referred to reviews highlighting the application of the model to anxiety research (Jokela & Hanin, 1999; Raglin & Hanin, 2000) and positive and negative emotions (Hanin, 1997, 2000b; Robazza, Bortoli, Zadro, & Nougier, 1998). Several constructive critiques of the approach are also available (Cerin, Szabo, Hunt, & Williams, 2000; Gould & Tuffey, 1996; Kamata, Tenenbaum, & Hanin, 2002; Landers & Arent, 2001; Zaichkowski & Baltzell, 2001). The sections below provide a brief overview of selected aspects of the IZOF model bearing directly on the prediction of athletic performance based on individualized emotion profiling.

THE IZOF MODEL The IZOF model, developed in the naturalistic setting of elite sport, holds that emotion is a component of the psychobiosocial state conceptualized as a situational, multimodal, and dynamic manifestation of total human functioning (Hanin, 1997, 2000b). Five basic dimensions (form, content, intensity, time, and context) describe the individually optimal and dysfunctional structure and dynamics of performance-related emotional experiences. Such descriptions first identify selected multimodal idiosyncratic markers of emotional states related to successful and poor performances. These markers then serve as individualized criteria to estimate actual emotional states by repeated assessments during the season. Compelling empirical evidence supporting the model provides several guidelines for predicting emotion-performance relationships. Identifying Optimal and Dysfunctional Emotional States It is crucial for accurate predictions to realize that each athlete has not only individually optimal emotional intensity (high, moderate, or low; Hanin, 1980) but also a specific constellation (Hanin, 1993, 1997, 2000b) or a “recipe” (Gould & Udry, 1994) of optimal and dysfunctional emotional content that is best described as athlete-generated markers. Moreover, idiosyncratic emotional content and intensity are different in practices and competitions and vary across pre-, mid-, and post-event performances (Hanin & Stambulova, 2002; Syrjä, Hanin, & Pesonen, 1995; Syrjä, Hanin, & Tarvonen, 1995). Our study focused on identifying the idiosyncratic content of emotions, bodily responses, and task-specific characteristics as performance predictors in precompetition situations. The other four components of the form dimension (cognitive, motivational, behavioral, and communicative) were not examined in this particular context. Several individualized assessment procedures are proposed to qualitatively and quantitatively identify optimal and dysfunctional emotional states (see Hanin, 2003, for details). These include semi-structured interviews (Orlick, 1986), self-report scales, individualized emotion and performance profiling (Butler & Hardy, 1992; Hanin, 2000a), metaphor-generating methods (Hanin & Stambulova, 2002), and narratives (Sparkes & Silvennoinen, 1999). The present

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study used an emotion profiling procedure to generate idiosyncratic markers of emotions, bodily symptoms, and performance characteristics. Prediction and Explanation of Emotion–Performance Relationships The IZOF model makes several empirically supported and individual-oriented predictions of emotion-performance relationships. First, there is a high degree of interindividual variability in the content and intensity of idiosyncratic optimal and dysfunctional emotions accompanying individually successful and poor performances. In other words, athletes would experience different content and intensity of optimal and dysfunctional emotions, and what is optimal for one athlete could be detrimental for another. Second, the impact of individually optimal and dysfunctional emotions upon athletic performance is predicted by contrasting current (or anticipatory) intensities of each emotion with previously established bandwidths (zones) of intensity. Third, separate and interactive effects of emotions enhancing and impairing sporting activity should be considered. Specifically, a high probability of individually successful performance is expected when combined maximum-enhancing and minimum-impairing effects (in-zone condition) are observed. In contrast, a high probability of poor performance is predicted when low-enhancing and high-inhibitory effects (out-of-zone condition) occur (Hanin, 1997, 2000b; Kamata et al., 2002). The in/out-of-zone notion in the prediction of performance has been successfully tested as applied to uni-dimensional and two-dimensional measures of anxiety (see Jokela & Hanin, 1999, meta-analysis for a review). Several studies also examined the practical utility of the in/out-of-zone notion applied to pleasant and unpleasant emotions in predicting performance (see Hanin, 2000c, for a review; Robazza et al., 1998; Robazza, Bortoli, & Nougier, 2002; Syrjä, Hanin, & Pesonen, 1995; Syrjä, Hanin, & Tarvonen, 1995). Our study attempted to extend the direction of this research by examining the predictive power of idiosyncratic emotions as well as bodily responses and task-specific characteristics. The zone concept of the IZOF model applied to emotions reflects individual differences in the athletes’ ability to efficiently recruit and utilize available resources. Therefore, the explanation of the functional impact of emotions upon performance is based on the notion of resource matching. Optimal pleasant and unpleasant emotions are related to the availability of resources and their effective recruitment and utilization; they would produce energizing (enhanced effort) and organizing (enhanced skill) effects. In contrast, dysfunctionally unpleasant and pleasant emotions imply the lack of resources and their inefficient recruitment and utilization, which would result in dis-energizing and dis-organizing effects upon performance. Similarly, emotionrelated bodily responses would involve effective (or ineffective) recruitment and utilization of available resources, and serve as predictors of successful and less than successful performance. In addition to emotions and bodily symptoms, performance characteristics important to succeed in a selected sport were introduced in this study as a new parameter. These task-specific qualities are supposed to reflect a total performance potential, that is, availability of individual resources without implying recruitment or utilization processes. In summary, the purpose of the present study was to examine the practical utility of the in/out-of-zone notion as applied to the idiosyncratic intensity and content of emotions, bodily symptoms, and task-specific characteristics to predict athletic performance. Based on previous research findings, we hypothesized that high-level athletes would be able to recall and identify their optimal and dysfunctional emotional states, bodily responses, and task-specific characteristics related to successful and less than successful performances. Moreover, the content and intensity of athletes’ significant experiences would be highly idiosyncratic and instrumental in predicting successful and less successful performances across the competitive season.

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Shotokan karate athletes were participants in this study. Karate, due to its short duration of fighting (a single round lasts 90 seconds with an additional 90 seconds of fighting only in case of a tie), was deemed especially appropriate to examine the functional impact of precompetition states upon performance. Thus, emotions are supposed to remain relatively stable from pre- to mid-event situations and this would enhance the predictive power of precompetiton states. For instance, in the Terry and Slade (1995) study mood and anxiety measures 40 minutes prior to a first-round fight discriminated well between winning and losing karate athletes. Thus, these investigators concluded that karate performance appears exceptionally mood-dependent. METHOD Participants Participants were ten elite, black-belt Shotokan karate competitors (karateka), eight males and two females, from northeast of Italy. They ranged in age from 17 to 27 years (M = 20.9 yr., SD = 4.3 yr.), practiced karate for eight to 18 years, and undertook at least four weekly practice sessions of two hours each. At the time of the study, eight athletes were members of the Italian national team and two candidates were expected to join the team. The athletes had extensive experience in national and international competitions in which they obtained prominent results. Five karateka won at least one Italian national competition in their careers and three of them ranked among the top three in international events. Only experienced, high-level athletes were chosen to take part in the investigation. Actually, our ten athletes represented almost all the best Italian karate athletes available at the time of the study. Experience and competition levels were supposed to affect the individual’s awareness of the content and functional effects of precompetition emotions leading to reliable estimates. The purpose of the research was explained to coaches and karateka at their practice sites. Informed consent was then obtained from participants who decided to take part in the study and from the parents of a 17-year-old athlete. Measures Task-Specific Characteristics Three lists of descriptors were developed to help athletes select or generate their own descriptors of sport-specific qualities needed to succeed in karate fighting (kumite). Three black-belt, high-level instructors and an expert physical trainer practicing karate identified the items of the stimulus lists in a brainstorming session. The procedure used to generate the specific qualities was based on the performance profiling approach of Butler and colleagues (Butler & Hardy, 1992; Butler, Smith, & Irwin, 1993). The specific question for discussion was “What are the physical, technical, and tactical abilities an elite karateka needs to excel in kumite?” After a three-hour session, the four experts identified a number of descriptors regarding: (a) physical qualities (e.g., execution speed of kizami-tsuki, and execution strength of mae-geri); (b) individual technical skills (specific actions) required to perform karate (e.g., gyaku-tsuki, and mawashi-geri giodan); and (c) individual tactical skills required to accomplish attacking and defensive fighting strategies (e.g., attacking with kizami-tsuki, and anticipating the opponent’s intentions with mae-geri). The three stimulus lists thus developed were then presented to athletes asking them to identify the qualities deemed necessary to excel in karate fighting. Doyle and Parfitt (1996, 1997) examined the psychometric characteristics of performance profiles in track and field athletes. These authors showed support for the predictive validity of


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the profile after a period of familiarization with the assessment procedure. Areas of perceived need emerging from the profiles enabled significant prediction of actual performance measures as well as athletes’ and coaches’ perceptions of performance scores (Doyle & Parfitt, 1996). Similarly, D’Urso, Petrosso, and Robazza (2002) provided evidence for performance differentiation and discrimination of rugby players based on sport-specific descriptors. Doyle and Parfitt (1997) again obtained support for the construct validity of the profile in track and field athletes. Decreased profile areas of perceived need were congruent with increased performance as athletes progressed from the preparation period to the competitive season. Emotion and Bodily Descriptors An idiographic emotion scaling in this study used two stimulus lists with positive and negative emotional and bodily descriptors. The scaling was based on the emotion step-wise method proposed by Hanin (1997, 2000a), in which athletes select personally relevant items or generate their own descriptors of emotional experiences associated with their recalled best and worst preperformance states (see Procedure section). The emotion list contained 70 positive and negative descriptors randomly arranged. Adjectives describing emotions used by Hanin and Syrjä (1995a, 1995b; see also Hanin, 2000a) with athletes were translated into Italian. Four Italian sport psychologists, who spoke English, were involved in translating the adjectives. Two of them translated the words into Italian and the other two translated them back into English. Translations were then checked with the help of a native speaker of English. In developing equivalent forms of psychometric scales in different languages, several validation procedures (blind back-translation, use of bilingual subjects, etc.) were employed. However, to native speakers, literal translations often sounded strange, inappropriate, or even meaningless. Therefore, in a few cases, when there was no exact equivalent in Italian, we relied on athletes’ spontaneous selection of descriptors, to make sure they captured their significant experiences. The emotion stimulus list with translated adjectives has been employed in several studies of Italian competitors drawn from different sports (e.g., Robazza, Bortoli, & Nougier, 1998, 2002; Robazza, Bortoli, Nocini, Moser, & Arslan, 2000). In a sample of skilled performers (Robazza, Bortoli, & Nougier, 1998), the words “focused”, “motivated”, and “determined” were among the most frequently selected facilitating-positive items, whereas “tense”, “worried”, and “aggressive” were among the most frequently selected facilitating-negative words. “Unfocused”, “nervous”, and “unconfident” were often identified as inhibiting-negative items, whereas “relaxed”, “cheerful”, and “secure” were among the most selected inhibiting-positive ones. The bodily symptoms list, containing 45 descriptors of pleasant and unpleasant bodily experiences concomitant with performance emotions, was developed and applied in a sample of referees and athletes (Bortoli & Robazza, 2002; Robazza & Bortoli, 2003). The most frequently selected facilitating-positive descriptors were those indicating “relaxed muscles”, “regular breathing”, and for movements, “vigorous”, “energetic”, and “smooth.” The most often identified facilitating-negative terms were those relating to “muscular tension”, “sweating”, “stomach butterflies”, “stomach tension”, and “accelerated heart rate”. Inhibiting-negative terms were ones of “physical exhaustion”, “loose legs”, “stiff movements”, “back pain”, and “headache”, while inhibiting-positive ones were “relaxed muscles”, “slow movements”, “yawns”, “feeling fresh”, (not “sweaty”), and “loose legs”. Although reliability and validity might be a concern when using any measurement, idiographic assessment procedures have certain advantages over group-oriented standardized tests. Specifically, idiographic assessments have high face and content validity; the content of selected or self-generated descriptors is highly relevant and meaningful for the athletes; and finally, the words have important connotations with performance-related experiences. Therefore,

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one should recognize that idiographic measurements offer a viable alternative in overcoming some of the limitations of normative procedures. For example, several researchers contend that the limited number of emotions usually assessed by the questionnaires may not adequately tap into the individual’s competitive experience (Hanin & Syrjä, 1995a; Robazza et al., 2000). Moreover, data about groups obtained by normative scales are often irrelevant to individual athletes. In contrast, the reliability of idiosyncratic measures has been demonstrated. In a sample of high-level soccer players, Hanin and Syrjä (1996) reported a reliability of idiosyncratic emotion scales. Mean intraindividual Cronbach alphas of the facilitating-positive, facilitatingnegative, inhibiting-positive, and inhibiting-negative emotion scales ranged from .54 to .90. In addition, significant correspondence between recalled and actual scores, and between predicted and actual scores was found in 76.5% and in 70.6% of the players respectively, thus providing evidence for the accuracy of recall and prediction in athletes. Robazza and Bortoli (2003) found Cronbach alphas ranging from .78 to .86 on emotion scales, and from .74 to .85 on bodily scales. Borg Category Ratio (CR-10) Scale In order to use the same scoring procedure for emotional, bodily, and sport-specific idiographic descriptors, a modified Borg Category Ratio (CR-10) scale was adopted as proposed by Hanin (2000a). The scale has been used in psychological studies of exercise capacity, exertion, or pain (see Borg, 2001), and for investigation of emotional intensity (see Hanin, 2000a, 2000c). The verbal anchors of the scale, developed to avoid floor and ceiling effects, were 0 = nothing at all, 0.5 = very, very little, 1 = very little, 2 = little, 3 = moderately, 5 = much, 7 = very much, 10 = very, very much, • = maximal possible (no verbal anchors were used for 4, 6, 8 and 9). Single item scores may range from 0 to 11. The scale allows one to make ratio comparisons on intensity ratings and to determine the magnitude of direct intensity. It can be used for intraindividual, interindividual, and group contrasts (Borg, 2001). The responses of 200 athletes who rated the intensity of their optimal, dysfunctional, and current emotional states (for a total of 46,934 intensity scores), revealed adequate distribution of verbal anchors along the intensity continuum (Tummavuori & Hanin, 2000). Procedure The objectives of the study were discussed with the athletes. Confidentiality regarding individual information was assured, and it was made clear that participation in the study could be discontinued at any time, should any disturbance in their performance be perceived. The investigation was carried out in two stages involving both emotional and performance profiling, and a repeated precompetition assessment. Emotion and Performance Profiling Athletes were met individually and provided with a form to help them develop and graphically display their own multidimensional profile of their best and worst preperformance states. Emphasis was placed on the benefits of the performance profiling procedure in helping each athlete develop individualized self-regulation strategies. Participants were asked to recall their best-ever competition, to describe how they felt prior to the first-round fight, and to identify their most relevant descriptors using the stimulus lists. Specifically, athletes had to choose three emotional descriptors and three bodily symptoms perceived as facilitatingpleasant, and three emotional descriptors and three bodily symptoms perceived as facilitatingunpleasant. Participants were asked to think about their worst-ever competition and describe how they felt prior to their first-round fight, and then to identify inhibiting-unpleasant and


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inhibiting-pleasant emotional and bodily descriptors. Twelve emotions and 12 bodily symptoms were thus selected. After that, athletes identified three physical, three technical, and three tactical characteristics deemed necessary for high achievements. A total individualized profile included 33 emotion, symptom, and task-specific items. Finally, athletes scored, on the modified Borg CR-10 scale, emotion, symptom, and task-specific items referring to recalled best preperformance conditions, and then referring to recalled worst preperformance states. Therefore, two intensity scores for each item reflected recalled best and worst precompetition states. Precompetition Assessment Precompetition repeated assessments were conducted over the entire competitive season collecting data for ten events. Participants were provided with a form for each competition containing their individualized 33-item scales. They were requested to score each item on the modified Borg CR-10 scale within the 15 minutes prior to the first-round fight with the “How do you feel right now?” instructions. Participants also evaluated their performance on the CR-10 scale immediately after the end of the first-round fight. This retrospective evaluation was restricted to the first event of a series of fights with the purpose of focusing on the link between precompetition states and performance, and to control for the effects of other factors (i.e., event duration and outcomes) that would moderate the predictive power of precompetition states. A self-referenced performance assessment was deemed appropriate for the study purposes since final results or jury decisions may not account for factors (such as the opponent’s ability) that cannot be easily quantified (D’Urso et al., 2002; Gould, Tuffey, Hardy, & Lochbaum, 1993; Terry, 1995). In summary, emotions, bodily symptoms, and task-specific qualities related to best-ever and worst-ever performances were identified and scored using a recall method. The 33-item individualized scale resulting from this procedure was then used in repeated actual assessments that involved (a) precompetition ratings of emotions, bodily symptoms, and task-specific qualities, and (b) immediate post-performance self-rating of individual performance using the CR-10 scale. RESULTS Participants identified a total of 42 emotional descriptors, 39 bodily descriptors, 17 physical qualities, 20 technical skills, and 14 tactical skills.1 All data of athletes across ten competitions were examined. Scores of items with a same content category (facilitating-pleasant emotions, facilitating-pleasant symptoms, etc.) were averaged, resulting in 11 variable raw data scores. Three performance levels were established based on the athlete’s self-ratings on the Borg CR-10 scale of current achievements across the competitive season. Performance scores of 100 events, for ten athletes with ten competitions each, were classified good (5–10 range), average (3–4), or poor (0–2). Thirty-four competitions were good (M = 5.82, SD = 1.40), 57 average (M = 3.54, SD = 0.50), and nine poor (M = 1.39, SD = 0.78). Poor competition data were excluded because of the small number, so that a total of 91 recorded observations were analyzed to differentiate actual good and average performances. Analysis was conducted on raw data and intraindividual data of the 11 content categories using performance level (good and average) as an independent variable. The advantages of intraindividual approaches, which were sensitive to within-subjects fluctuations, have been 1

Descriptive results of idiosyncratic profiles and ANOVA results of the interactive effects of emotions and symptoms are available from the first author.

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recognized and advocated by several authors. Intraindividual analyses were applied to examine the anxiety-performance relationship in different sports, for example in basketball (Sonstroem & Bernardo, 1982), pistol shooting (Gould, Petlichkoff, Simons & Vevera, 1987), and swimming (Burton, 1988). Similarly, the value of using difference scores in the analysis was demonstrated in studies testing the zone notion of the IZOF-emotion model (e.g., Prapavessis & Grove, 1991; Robazza, Bortoli, & Nougier, 2002; Robazza, Bortoli, Zadro, & Nougier, 1998; Syrjä, Hanin, & Pesonen, 1995; Syrjä, Hanin, & Tarvonen, 1995).2 For all analyses, alpha level for main effects and follow-up was set at .05. A more stringent alpha level, to avoid Type I errors, was not deemed necessary since planned comparisons were based on a sound theoretical framework and the results of previous work. Cohen’s measure of effect size (d) has also been reported for follow-up tests. Raw Data Analysis Raw data analysis of emotions and bodily symptoms yielded significant results, Wilks’s λ = .73, F(8, 82) = 3.70, p < .001, η2 = .27. Univariate ANOVA follow-up revealed significant differences on facilitating-pleasant emotions, F(1, 89) = 15.07, p < .001, η2 = .14, d = 0.84, facilitating-unpleasant emotions, F(1, 89) = 9.63, p < .003, η2 = .10, d = 0.67, inhibitingunpleasant emotions, F(1, 89) = 4.41, p < .04, η2 = .05, d = 0.45, and inhibiting-unpleasant bodily symptoms, F(1, 89) = 6.83, p = .01, η2 = .07, d = 0.56. Mean raw scores of significant variables characterizing good performance were higher compared to average performance, whether items were facilitating or inhibiting (see Table 1, Raw Scores column). Thus it was possible to differentiate between performances classified as good and average. MANOVA on perceived quality descriptors did not reach significance. Intraindividual Analysis Using the Direct Method Actual best and worst performances were chosen for each athlete among the ten retrospectively assessed to compute intraindividual scores with the direct method. Best performance scores ranged 4 to 11 (M = 6.40, SD = 2.22), whereas worst performance scores ranged 0 to 3 (M = 2.00, SD = 1.05). Thereafter, two absolute difference scores for each dependent variable (emotions, symptoms, and task-specific qualities) were computed, subtracting (a) the score relating to actual best performance from the score relating to current assessments (91 observations, 34 good and 57 average achievements); and (b) the score relating to actual worst performance from the score relating to current assessments (91 observations). Scores were computed as absolute values because the focus of the IZOF model is on the magnitude rather than the direction of difference scores. MANOVA results on actual best minus current performance scores of emotional, bodily, and perceived quality descriptors did not reach significance. In contrast, MANOVA results on actual worst minus current performance scores of emotional and bodily descriptors were significant, Wilks’s λ = .81, F(8, 82) = 2.45, p < .02, η2 = .19. Follow-up revealed significant effects for facilitating-pleasant emotions, F(1, 89) = 14.17, p < .001, η2 = .14, d = 0.82, and facilitating-pleasant symptoms, F(1, 89) = 5.02, p < .03, η2 = .05, d = 0.49. According to 2 It should be acknowledged that some limitations are associated with this approach. Edwards (1995), for example, raised concerns on the use of difference scores as dependent variables. He ascribed the major problems of difference scores to low reliability, conceptual ambiguity, confounding effects of the independent variables on the components of the difference, and transformation of an inherently multivariate model into a univariate model.


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Table 1 Means and Standard Deviations of Dependent Variable Raw Scores and Intraindividual Scores (Actual minus Current Performance Scores, and Recalled minus Current Performance Scores) by Performance Level

Content categories and performance level Facilitating pleasant emotions Average performance Good performance Facilitating pleasant symptoms Average performance Good performance Facilitating unpleasant emotions Average performance Good performance Facilitating unpleasant symptoms Average performance Good performance Inhibiting unpleasant emotions Average performance Good performance Inhibiting unpleasant symptoms Average performance Good performance Inhibiting pleasant emotions Average performance Good performance Inhibiting pleasant symptoms Average performance Good performance Physical factors Average performance Good performance Technical skills Average performance Good performance Tactical skills Average performance Good Performance

Actual Actual Recalled Recalled Raw Scores Best–Current Worst–Current Best–Current Worst–Current M

SD

M

SD

M

SD

M

SD

M

SD

4.22 5.27

0.98 1.61

0.89 1.00

1.18 1.53

0.81 1.68

0.86 1.34

1.54 1.87

1.58 1.67

3.03 4.13

1.11 2.02

3.69 3.94

1.26 1.31

1.12 0.94

1.06 1.46

0.96 1.61

1.22 1.52

2.15 1.69

1.26 1.53

2.64 2.92

1.31 1.40

2.75 3.94

1.68 1.93

0.87 0.85

0.95 1.49

0.97 1.64

1.21 2.11

1.54 1.65

1.53 1.83

1.64 1.99

1.46 1.88

2.75 3.25

1.27 1.34

1.06 0.86

0.84 0.86

0.91 0.93

0.85 0.82

1.65 1.25

0.92 1.01

1.69 2.69

1.51 2.47

2.19 2.77

0.99 1.65

0.62 0.78

0.49 0.75

0.73 0.94

0.62 0.82

1.41 1.61

0.92 0.97

2.65 4.23

2.66 3.24

2.31 3.04

1.22 1.43

0.90 0.85

0.81 0.76

0.86 1.10

0.90 0.87

2.06 2.07

1.58 1.51

2.22 2.86

1.77 2.23

2.48 2.67

1.08 1.13

0.67 0.57

0.59 0.66

0.75 0.88

0.73 0.83

1.39 1.79

0.90 1.10

1.31 1.06

0.84 0.78

2.75 3.07

0.95 0.95

0.98 0.93

0.93 0.98

0.85 0.95

0.78 0.85

1.31 1.25

0.98 1.08

1.82 1.86

1.05 1.35

4.40 4.81

1.37 1.67

0.72 0.85

0.75 1.29

0.84 1.12

0.68 1.10

1.24 1.23

0.68 0.93

2.56 2.75

1.88 2.31

4.63 4.97

1.14 1.38

0.82 1.00

0.73 1.15

0.98 1.32

0.81 1.15

1.33 1.48

0.88 1.36

2.75 2.78

1.38 1.65

4.27 4.74

1.49 1.83

0.94 1.01

0.87 1.33

0.90 0.99

0.84 1.29

1.54 1.60

1.14 1.22

2.37 2.29

1.42 1.56

predictions of the zone notion, item mean scores of good performance were larger than average performance for both emotions and symptoms, that is, more distant from worst performance (see Table 1, Actual Worst–Current column). Contrary to what was revealed in the raw scores analysis, the differences between intraindividual scores on inhibiting items were not significant. Moreover, no significant findings emerged on perceived quality descriptors. Intraindividual Analysis Using the Recall Method Besides the direct method, the recall method was used to compute intraindividual scores. Two absolute difference scores for each dependent variable were again derived by subtracting (a) the score relating to recalled best performance from the score relating to current assessments

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(91 observations); and (b) the score relating to recalled worst performance from the score relating to current assessments (91 observations). MANOVA results on recalled best minus current performance scores of emotional, bodily, and perceived quality descriptors were not significant; while MANOVA results on recalled worst minus current performance scores of emotional and bodily descriptors reached significance, Wilks’s λ = .82, F(8, 82) = 2.24, p < .04, η2 = .18. Follow-up revealed significant effects for facilitating-pleasant emotions, F(1, 89) = 11.27, p < .002, η2 = .11, d = 0.73, facilitatingunpleasant symptoms, F(1, 89) = 5.70, p < .02, η2 = .06, d = 0.52, and inhibiting-unpleasant emotions, F(1, 89) = 6.33, p < .02, η2 = .07, d = 0.55. These findings paralleled those obtained by intraindividual analysis using the direct method. Consistent with the zone principle predictions, mean scores of significant variables (inhibiting items included) relating to good performance were more distant from recalled dysfunctional states than average performance (see Table 1, Recalled Worst–Current column). Here again, perceived quality descriptors did not yield significant results.

DISCUSSION The main purpose of the study was to examine whether the in/out-of-zone notion of the IZOF model could be applied to idiosyncratic emotions, bodily symptoms, and task-specific characteristics in highly skilled karate athletes. Empirical findings reported in the extant literature provide empirical evidence for the validity and practical utility of the concept of zone using idiosyncratic emotional descriptors (e.g., Robazza, Bortoli, & Nougier, 2002; Robazza, Bortoli, Zadro, & Nougier, 1998; Syrjä, Hanin, & Pesonen, 1995; Syrjä, Hanin, & Tarvonen, 1995). However, idiosyncratic bodily symptoms were not included in IZOF-based investigative protocols testing the zone notion. In this study, intraindividual analyses of bodily symptom scores and emotional scores yielded performance differentiation in accordance with the in/outof-zone predictions. Performance differentiation was also revealed on raw data of several variables (facilitatingpleasant emotions, facilitating-unpleasant emotions, inhibiting-unpleasant emotions, and inhibiting-unpleasant bodily symptoms). Compared to average performances, higher mean scores of emotions and bodily symptoms were associated with good performances whether the emotional states were facilitating or inhibiting. The beneficial effects of a high intensity of facilitating emotional states were self-evident. In contrast, the findings of inhibiting items, expected to be lower in the case of average performance, would appear atypical or difficult to explain. Despite what was obtained with raw score analysis, intraindividual analysis revealed that difference scores of inhibiting emotional states, derived by using the direct method, did not reach significance. Furthermore, significant results were obtained using the recall method with difference scores of inhibiting emotions in the expected direction. Specifically, mean scores of inhibiting emotions relating to current good performance, compared to current average performance, were more distant from recalled worst performance scores. Overall findings revealed that good achievements were associated with precompetition emotional states distant from dysfunctional (actual or recalled) individual zones, whether emotions were facilitating or inhibiting. This is in line with the in/out-of-zone notion (Hanin, 1997, 2000c) and the findings obtained in several empirical studies (Robazza, Bortoli, & Nougier, 2002; Robazza, Bortoli, Zadro, & Nougier, 1998; Syrjä, Hanin, & Pesonen, 1995; Syrjä, Hanin, & Tarvonen, 1995). Significant findings emerged contrasting intraindividual data resulting from worst (actual or recalled) performance minus current performance scores. Results were not significant in intraindividual data derived from best (actual or recalled) performance


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minus current performance scores. The explanation may lie in the generally satisfying levels of competitors’ achievements across events, which yielded difference scores of worst minus current performance data larger than best minus current performance data. Contrary to what was found for emotions and bodily symptoms, task-specific characteristics did not differentiate between karate performances on raw data or intraindividual data, apparently because analysis excluded poor competition scores. Our results were in agreement with previous research which showed that tactical skill constructs did not differentiate best and intermediate performances of rugby players (D’Urso et al., 2002). In particular, tactical skill raw scores of the best and intermediate games were not different, although both were significantly higher than scores of the worst games. A possible explanation may be related to the features of a situational sport such as karate fighting. For example, a karateka may score highly on personal construct items prior to a contest as a consequence of a satisfying physical and technical preparation. However, the superiority of an opponent or other uncontrollable factors (e.g., referee’s evaluation) are likely to bias the immediate post-performance retrospective assessment. As a result, an objectively good level of fighting performance may be retrospectively self-rated as less than satisfactory or average. This may moderate the predictive power of precompetition perceived qualities in differentiating self-evaluated good and average accomplishments. A different reason may explain the lack of differentiation between good and average performances on perceived task-specific quality scores. The assessment procedures to establish optimal/non-optimal zones of descriptor intensities probably provided a “rough” estimation of the zones. In the direct method, just two competitions, the best and the worst, were taken as reference points to contrast current assessments. In the recall method, current assessments were compared to recalled best and worst precompetition conditions established with the athlete. Perceptions of individuals’ optimal/non-optimal conditions to perform are supposed to change across a competitive season according to physical and technical changes. Moreover, even experienced athletes may need some time to develop awareness of factors leading to success and failure. Time is also necessary for athletes to become familiar with the assessment procedures, and then provide accurate and valid evaluations. Reliable and sensitive profiles are expected to develop over time through repeated assessments and refinements (Doyle & Parfitt, 1996; Hanin, 2000a). Therefore, researchers and practitioners are advised to monitor sportspecific characteristics and emotional states after a phase of development and improvement of idiosyncratic measures. Performance differentiation obtained for emotion and symptom scores can be interpreted within the framework of the IZOF model. Precompetition emotions and bodily symptoms are components of the psychobiosocial state; they are associated with dynamic processes of situational recruitment and utilization of available individual resources. Emotional states are dynamic and may fluctuate widely from pre-, mid-, to post-event (D’Urso et al., 2002; Hanin & Stambulova, 2002; Syrjä, Hanin, & Pesonen, 1995; Syrjä, Hanin, & Tarvonen, 1995). In contrast, sport-specific characteristics (i.e., qualities, abilities, or capacities) are trait-like, relatively stable factors reflecting the important developmental potential of the athletes and the availability of resources. Although they are needed to excel in a particular sport, these characteristics reflect more on an athlete’s general potential than how this potential is used via the processes of energy production (enhancing effort) and energy utilization (organizing behavior). Therefore, highly developed sport-specific qualities do not necessarily guarantee successful performance in either one or many competitions. That is why optimal emotional states can sometimes compensate for the lack of outstanding resources by providing energy for skillful performance or decision-making processes. By their very nature, trait-like factors are important from a long-term developmental perspective, but are expected to be less sensitive

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than dynamic situational factors in the prediction of an individual performance. The present findings lend initial empirical support for this contention. More research is needed to test the feasibility of combining dynamic-states assessments with dispositional performance measures and to improve the predictive validity of such assessments. Therefore, the preliminary findings in this study should be interpreted with caution. The relatively small size of the sample was not an issue in our study, which focused on intraindividual aspects of emotional and bodily experiences as related to successful and less than successful performances of almost all available top-level Italian karate athletes. However, future research should include a larger number of participants and be conducted across different sports to enable the researcher to make safer generalizations. Qualitatively oriented studies are also recommended (Hanin, 2003). In addition, there is a need to explore the interaction of other components of the psychobiosocial state and their contribution in describing, explaining, and predicting the athletic experience and sport achievements. Research within the IZOF psychobiosocial framework should be conducted in a holistic perspective by integrating emotional, bodily, and motivational components, as well as sport-specific performance variables. This would result in a multidimensional picture best representing the single effects of each component and their total functional/dysfunctional impact. Practical Implications The development of idiosyncratic multidimensional profiles has merits for several applied purposes. Individualized profiles are beneficial in helping athletes enhance their awareness of functionally optimal and dysfunctional states, identify specific goals for improvement, increase commitment in attaining these goals, monitor performance dynamics during contests, and facilitate post-performance analyses (Butler & Hardy, 1992; Butler et al., 1993; Jones, 1993; Hanin, 2000c; Weinberg & Williams, 2001). From an applied perspective, the present findings also indicate that coaches, athletes, and sport psychologists should realize that bodily experiences are not limited to negative symptoms, such as those that may be associated with precompetition anxiety. Bodily signals could be pleasant or unpleasant and functionally optimal or dysfunctional. Athletes should learn to “read” these idiosyncratic signals well enough to be able to control their emotional states. Based on idiosyncratic emotional and bodily symptom descriptors, a coach can individualize practices and enhance the quality of preparation of an athlete and/or team for competition. The present findings also suggest that coaches’ predominant focus on physical, technical, and tactical aspects of performance may not always be sufficient in helping athletes achieve a consistently successful performance. In other words, the athlete and coach should emphasize not only the development of sports-specific qualities but also their effective recruitment, utilization, and recovery. This becomes possible by improving the awareness of athletes’ optimal and dysfunctional emotional states prior to, during, and after competition. In the D’Urso et al.’s (2002) investigation, some interviewed rugby players acknowledged spontaneously the advantages of performance profiling in enhancing their awareness and achievement motivation. In following suggestions derived from the IZOF model, one’s awareness of the functional impact of dynamic situational factors, achieved by emotional profiling and repeated assessments, is the first step in the self-regulation of emotional states. Once this awareness is attained, the athlete can be helped to develop and implement self-regulation procedures to reach individually optimal states. The same concept can be extended to sport-specific qualities in a long-term developmental perspective. For example, the coach can assist an athlete who for some reason is far from optimal technical or physical condition, in establishing adequate performance goals and in planning proper training sessions. According to Dale and Wrisberg (1996), a


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performance profiling technique can be particularly useful in creating an open atmosphere for communication in which the coach and athlete have an opportunity to identify and discuss areas in need of improvement, and design strategies to address weaknesses. Coach and athlete can evaluate progress periodically by rating, recording, and displaying graphically the level of the strength/weakness descriptors. In this way they can estimate goal-attainment levels, assess the efficacy of training regimens, and adjust goals and strategies. From the perspective of the applied sport psychologist, preliminary evidence for the advantages of IZOF-based multimodal interventions has been provided in two intervention studies. The first study involved two skilled tennis players in the treatment of multidimensional anxiety (Annesi, 1998). In a second study, adopting a multiple single-subject design, the psychological treatment was extended to target emotional and bodily symptom modalities (Robazza, Pellizzari, & Hanin, in press). A multimodal self-regulation intervention was applied to experienced hockey players and gymnasts across a competitive season to help them consistently reach their optimal zones and thus attain performance benefits. Essentially, the treatment was intended to enable athletes to improve their awareness of how different contents and intensities of precompetition emotions and bodily symptoms exert facilitating or inhibiting effects. Later, self-regulating procedures were systematically applied to recover and modulate the intensity of optimal precompetition emotions and bodily symptoms. Study findings provided support for the beneficial effects of multimodal self-regulation strategies to optimize precompetitive states and improve competitive achievements. Though preliminary, these results support the contention that mental training should address all the different modalities (i.e., cognitive, emotional, motivational, bodily, behavioral, operational, and communicative) matching them to the individual’s needs for improvement. To develop the most effective intervention strategies, future research should explore additional target areas (e.g., cognitive, motivational, behavioral, and communicative) incorporated in the recent developments of the IZOF-psychobiosocial model.

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