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Mood scores: mood and performance in professional cricketers.

To what extent are variations in the performances of sports competitors owing to variations in their moods? Although some star sports performers are able to produce remarkably consistent performances, most have troughs and peaks in their performances. Variations in sports performance from one performance to the next clearly cannot be because of skill acquisition and loss, but might at least partly be explained by concomitant changes in the performer's mood states. This study evaluates this proposition by examining whether changes in different types of mood are associated with changes in professional cricketers' performance.

The term 'mood' is normally used to refer to affective (or feeling) states that are less intense, more persistent, and less directed than emotions. There is considerable agreement that the structure of mood can be represented as a circumplex, in which mood terms are arranged around the circumference of a circle, the principal axes of which represent two dimensions of mood (e.g. Feldman, 1995; Watson & Tellegen, 1985). In some versions of the model, the two dimensions represent valence and arousal (Feldman, 1995). 'Valence' refers to the hedonic tone of an experience and includes states such as pleased and happy; 'arousal' refers to the perception of arousal and includes states such as aroused and energetic. However, other versions of the circumplex model focus on the rotated dimensions of positive affect and negative affect (Watson & Tellegen, 1985). 'Positive affect' refers to pleasurable engagement and includes states such as excited and enthusiastic; 'negative affect' refers to feelings of distress and includes states such as tense and anxious.

Mood is known to have effects on a range of processes including perception, reasoning, memory and behaviour (see Parkinson, Totterdell, Briner & Reynolds, 1996), all of which may be involved in determining performance outcomes. Matthews (1992) identified two areas where consistent effects of mood on performance have been found: the facilitating effects of energetic mood on information processing efficiency, and the facilitating effects of hedonic tone (pleasantness of mood) on processing of mood-congruent information.

Positive mood, which includes states of high arousal and high pleasure, has been linked to a range of performance-related behaviours including greater helping behaviour, enhanced creativity, more efficient decision making, greater cooperation and the use of more successful negotiation strategies (Baron, 1990; Forgas, 1998; Staw & Barsade, 1993). With respect to occupational performance, positive mood has been positively associated with sales-related prosocial behaviours (George, 1991) and job performance ratings (Staw, Sutton & Pelled, 1994). In contrast, negative mood, such as fatigue induced by extended or abnormal work hours, has been shown to have a negative association with job performance (Monk, 1990).

Research in the sports domain has usually concentrated on precompetition mood (especially anxiety) as a predictor of competition outcome, with mixed results (see Terry, 1995; Terry & Slade, 1995). Researchers have had some success using scores on the profile of mood states (POMS) questionnaire (McNair, Lorr & Droppleman, 1971) to discriminate between winners and losers in some sports (e.g. Fung & Fu, 1995; Terry & Slade, 1995), but not in all sports (e.g. Terry & Youngs, 1996). A meta analysis of results from sport studies using POMS found that successful athletes do generally have a slightly more positive mood profile, but that the effect is small (Rowley, Landers, Kyllo & Etnier, 1995). The association between precompetitive POMS scores and subsequent performance may also only hold for some individuals (Hassmen, Koivula & Hansson, 1998). A more general problem with interpreting these findings is that more successful athletes may have a more positive mood profile before competition precisely because they know from past experience that they are likely to be successful in the forthcoming competition. It would be of more interest to know whether performers have a more positive mood profile before their own more successful performances compared to their own less successful performances, but again their judgment of likely success may affect their mood before competition.

Hanin & Syrja (1995a, 1995b) used a different approach to the problem, known as the individual zones of optimal functioning (IZOF) model, in which players identify their optimal and non-optimal intensity ranges on personally selected mood scales using their recall of moods during successful and unsuccessful performances. Results from the IZOF model have shown that successful players deviate less from their optimal mood intensity ranges (Hanin & Syrja, 1995b).

A mood-related variable that has received much attention in sport psychology research is self-efficacy (Biddle, 1997). Self-efficacy refers to a person's beliefs about accomplishing a task and can influence choice of activities, effort, persistence and achievement (Schunk, 1995). The difference between self-efficacy and self-confidence is that self-efficacy is specific to situations rather than global (Carton, 1988). In other words, someone can have high self-efficacy for one skill but low self-efficacy for another. Research evidence suggests that self-efficacy is an important determinant of performance attainment in sport (e.g. Boyce & Bingham, 1997; Swain, 1996; Theodorakis, 1995). This relationship is independent of the performer's motivation (Miller, 1993). There also appears to be a relation between self-efficacy and mood state prior to competition (Treasure, Monson & Lox, 1996).

Another variable that has attracted interest for its potential to predict sporting performance is mental focus. This usually refers to people's ability to narrow their attention so that they concentrate on a task and withstand distractions. The capacity for selective attention is commonly treated as a perceptual or cognitive ability, but attentiveness also has an affective connotation and has been used in measures of positive affect (e.g. Watson, Clark & Tellegen, 1988). Research suggests that greater levels of focus are associated with more successful sports performance (Howe, 1993; Molander & Backman, 1994; Wilson & Kerr, 1991).

In general, however, research concerning relations between mood and performance in professional and amateur sports has produced ambiguous results. A possible difference between professional sports and other jobs is that professional sport requires players to seek a maximum level of performance whenever they are performing, whereas many jobs tolerate temporary periods of sub-optimal performance. This means that professional sports players must always try to resist any debilitating effects of mood and capitalize on any facilitative effects.

Another possible difference between performance in professional sports and many other jobs is that sports performance may rely more on lower-level automatic processing than on upper-level processing, such as planning and reasoning. In relation to this possibility, research evidence suggests that the effects of energetic mood (and perhaps even hedonic mood) may be confined to upper-level processing (Matthews, 1992). However, there are considerable difficulties in relating particular performance indices to particular levels of cognitive control (Chmiel, Totterdell & Folkard, 1995).

A third possibility is that methodological shortcomings have produced ambiguous results in the sports domain. Mood measures have had most success in predicting performance in sports that are short in duration, involve closed rather than open skills, and involve individuals rather than teams, and in groups with homogenous abilities (Terry, 1995; Terry & Slade, 1995; Terry & Youngs, 1996). Failure to find mood effects in other sports and groups may have occurred because other influences, such as mood fluctuation, interpersonal influence and variations in skill level, exert effects that exceed those of pre-performance mood. For example, pre-performance mood is particularly unlikely to predict performance in long duration sports, such as cricket, because of the amount of time during which other influences can intervene to affect performance. Terry and colleagues also point out that most investigations have used between-subject cross-sectional designs to compare the mood and performance of different competitors, rather than using within-subject longitudinal designs to assess intraperson relations between mood and performance. They also note that most investigations have used objective global measures of performance and may therefore be insensitive to self-referenced variations in performance.

The main aim of the present study, therefore, was to determine whether relations between mood and performance could be found in a long duration professional team sport using recommended methodological improvements. In particular, the study examined whether professional cricketers' performances changed in association with different aspects of their mood over the course of a cricket match; whether players whose moods were more positive produced better performances; whether players' moods before performance predicted their subsequent immediate performance; and whether players' recollection of their moods during their best and worst performances corresponded with their actual mood-performance relations during a match.

The sport of cricket was chosen for study because it involves matches that can take up to 4 days to complete and has scheduled breaks between sessions of play, which made it feasible to collect measures of players' mood and performance at regular intervals during a match. The collection of multiple variables from multiple persons on multiple occasions is known as 'intensive time-sampling'. This type of design meets Terry & Slade's (1995) recommendations that within-subjects longitudinal designs should be used to study the mood-performance relationship in sport. The use of within-subjects analysis was complemented by a between-subjects analysis of relations between aggregate mood per player and performance.

The study also complies with Terry & Slade's (1995) recommendation to use subjective performance measures. A cricket match produces a variety of objective performance measures for individual players, such as the number of runs a batter scores or the number of wickets a bowler takes (which is the number of batters whose batting turn is terminated by the bowler). However, objective game scores in sport can be misleading (Sewell, 1996; Terry & Slade, 1995). In cricket, for example, the value of the number of runs a batter scores depends on the quality of the pitch, atmospheric conditions, the quality of the bowler, the match situation and the role of the player. Cricketers can also make incomplete and multiple performance contributions (batting, bowling and fielding) during sessions of play. This implies that various types of match score would have to be transformed and combined in some way to produce a measure of performance for a session of play. Asking players to rate their own performance may be a better alternative because they can use their playing experience to judge the quality of their performance.

A subjective performance measure, however, may be contaminated by players' own moods. In other words, players may rate their performance according to how they feel at the time of rating. Indeed, players might use this method because they believe that their moods are a good indicator of their performance. Other researchers, however, have argued that reports of events are not biased by mood (e.g. Seidlitz & Diener, 1993), that mood congruent effects are not pervasive (e.g. Parkinson et al., 1996), and that the contamination problem has been overestimated (e.g. Lu, 1991). Nevertheless, in light of this potential problem, the current study used a dual approach to assess match performance: players' self-assessment of their performance was used to assess changes in performance during a match, but objective measures were used to assess overall match performance. The two types of performance measure were also compared.



The participants were 33 male professional cricketers whose ages ranged from 19 to 37 years (M = 26). The cricketers were members of four cricket teams (Teams A, B, C and D). Teams A, B and D were competing in the English County Championship; Team C was competing in the English Second XI Championship. The teams had experienced different degrees of success in the previous season.

The investigation was described to the players as a study of mood and performance. Participation was voluntary and had no financial incentive. Of the 11 players in each team, 9 players from teams A, B and C and 6 players from team D took part. One player from each team was not available at the time of the study training session and so could not participate, but the other non-participants chose not to take part for unknown reasons.


Each team was studied for the duration of one championship match (during which each team bats twice). Teams A and B were studied during a match in which they played each other; the match lasted 4 days and resulted in a draw. Teams C and D were studied during separate matches, both of which lasted 3 days and resulted in a win for C and a loss for D. Play was scheduled to begin at 11:00 a.m. and end at 6:30 p.m. every day, with a lunch interval of 40 min at about 1:15 p.m. and a tea interval of 20 min at about 4:10 p.m. The intervals therefore divided the day into three sessions of play of approximately equal duration.

Each player was given a pocket computer on which to record his responses during the study. A detailed description of this instrument and its use in intensive time-sampling studies was given by Totterdell & Folkard (1992). The players were asked to use the pocket computer before play began, during the lunch and tea intervals, and at the end of play each day. In general, the players responded favourably to using the computers and one coach commented that his players had enjoyed using them.

One response occasion was lost from the match between teams A and B and from the match involving team D because of an early finish on one day of the match. All players responded on at least five occasions and overall the players responded on 65% of possible occasions. However, there were very few responses during the tea intervals because the players usually had insufficient time to complete the measures. Excluding the tea intervals, the participating players responded on 81% of possible occasions. Response compliance was as high as 95% for the occasions before play began and as low as 69% for the final day of the match, which suggests that time pressure and study fatigue affected response compliance. Csikszentmihalyi & Larson (1987) reported that 80% is the average response frequency for time-sampling studies of this type, but response compliance can drop to 65% when there is time pressure on participants (Totterdell, Kellett, Teuchmann & Briner, 1998).


Players used the pocket computers to complete a series of bipolar and unipolar rating scales. The rating scales took under 5 min to complete. The players had to select one of 19 possible positions along each scale to represent either their current feelings at the beginning of the day or their feelings during the session of play just completed (depending on the time of completion). The measures used in the present study are a subset of the complete battery of measures.

Mood. Players used six bipolar scales to rate their own mood. The adjectives at the ends of three of the scales were chosen from three dimensions of the UWIST Mood Adjective Checklist (Matthews, Jones & Chamberlain, 1990). These mood scales (and the two adjectives at the endpoints of the scale) were hedonic tone (unhappy-happy), energetic arousal (sluggish-energetic) and tense arousal (calm-tense). In terms of the circumplex model of mood, these scales correspond to the dimensions of valence, arousal and negative affect. A fourth scale for enthusiasm (bored-enthused), corresponding to the dimension of positive affect, was also included. In other words, a mood adjective from each of the eight main poles of the mood circumplex model was used. Two other bipolar scales were also included because of their potential relevance to sports performance. These scales were focus (distracted-focused) and self-efficacy (doubtful-confident). As well as using the six scales as separate measures of mood, they were also combined into an overall measure of general positive mood ([Alpha] = .84). The tense arousal scale was reverse scored for this purpose.

Mood during best and worst performances. At the start of the study, the players were asked to recall how they felt during their best performance (or one of their best performances). They were asked to rate their feelings during that performance using the six mood scales described above. This procedure was then repeated for their worst performance. Previous studies have shown that elite performers are able to reproduce their feelings during competition (Hanin & Syrja, 1995 b). It has also been shown that recall-based ratings of emotion do contain accurate information about momentary experiences, but that they can be influenced by personality characteristics (Feldman Barrett, 1997).

Subjective performance. Players used two bipolar scales at the end of each session of play to rate their personal performance and their team's performance during the session (bad-good). These scales were not relevant at the beginning of a day and were not therefore presented. The scale for personal performance was also not relevant when a player had been waiting to bat during a whole session of play. For these reasons, there were fewer responses for subjective performance ratings than for the other ratings.

To test whether the players' ratings of team performance were consistent within teams, a procedure for estimating within-group interrater agreement (James, Demaree & Wolf, 1984, 1993) was applied to the mean of the standard deviation of the scores per team per occasion. The within-group interrater agreement ([r.sub.wg]) estimate was .85, which demonstrates a high level of consistency in the team performance ratings. This level of agreement implies that the individual team ratings can be used as an estimate of team performance.

Objective performance. Performance measures for each player were derived from the scorecards for the studied matches. The two measures that are most commonly used to describe cricketers' overall performance are batting average and bowling average. Batting average refers to the number of runs a batter has scored divided by the number of times the batter has been dismissed (batting turn terminated). Bowling average refers to the number of runs a bowler has conceded divided by the number of wickets the bowler has taken. A successful batter usually obtains a large batting average, whereas a successful bowler usually obtains a small bowling average. The batting and bowling averages of the players were therefore calculated for the studied matches.

In order to obtain an objective measure of whether players had performed better or worse than in the past season, difference scores were calculated by subtracting players' batting and bowling averages for the whole of the previous season (published in Engel, 1997) from their batting and bowling averages for the studied match.

Overview of main analyses

A pooled time-series analysis method was used to test whether players' moods and subjective performances were associated. This involved using a least squares dummy variables regression model with performance or mood as the dependent variable. Details of this analysis procedure can be found elsewhere (see Alliger & Williams, 1993; Totterdell, Parkinson, Briner & Reynolds, 1997; West & Hepworth, 1991).

The pooling of time series from different participants requires the removal of between-subjects variance from the data so that it is amenable to a within-subjects analysis. This was achieved by entering dummy variables to represent N - 1 participants into the regression model to remove differences in level between different players' time series.

Before analysing the time series data, it was necessary to remove any trend, cycle and serial dependency from the time series in order to meet the assumption of independence between observations. The following control variables were therefore constructed for entry into the regression models: a variable for the number of days into the study to control for any linear trend arising from repeated response to a measure; a variable for the time of response (using the values 1-4 to represent the four response occasions on each day) to control for time-of-day effects; and a variable for the previous value (first-order lag) of the dependent variable to remove serial dependency in the time series.

Within-subject correlations between mood and subjective performance were also calculated for each participant. The other analyses involved between- rather than within-subject analysis. Some of these analyses required prior calculation of aggregate mean mood and mean subjective performance scores for each participant. For example, scores for the bowlers' average mood during sessions in the match in which their main activity was bowling were calculated, so that they could be correlated with their bowling average for the match.


Table 1 shows the correlations between the mean mood and mean subjective performance ratings for each player during the match. The correlations involving tense mood were negative, but all the other correlations were positive.
Table 1. Intercorrelations of cricketers' mean mood and mean
subjective performance during a match (N = 33)

Variable             1       2     3      4      5     6      7     8

1. Happy             -
2. Tense           -.61     -
3. Energetic        .65    -.52   -
4. Enthused         .66    -.36   .85    -
5. Focused          .49    -.37   .80    .79    -
6. Confident        .76    -.69   .56    .46    .52   -
7. Positive Mood    .83    -.65   .90    .86    .84   .80    -
8. Performance      .60    -.41   .54    .38    .47   .32    .55   -

Note. r [greater than] .34, p [less than] .05; r [greater than] 44,
p [less than] .01; r [greater than] .54, p [less than] .001.

Within-player relations between moods and subjective performance

A pooled time series regression analysis was conducted to determine whether changes in the players' performances during the match were associated with their moods. The details of this type of analysis, including descriptions of the control variables used, are described in the overview of analyses. Regression models were tested using players' ratings of their own performance for each session of play as the dependent variable and their concurrent ratings of their moods for the same session of play as the independent variables. Each mood variable was tested in a separate model. The models included a control variable for performance during the previous session and hence tested whether players' moods during a session of play predicted a change in their performance from the previous session of play to the current session. However, players' performance during the previous session did not predict their current performance. The results are shown in Table 2.

Table 2 shows that players performed better when they were in a more positive mood. More specifically, they performed better when they happier, more focused, [TABULAR DATA FOR TABLE 2 OMITTED] more energetic, or more enthusiastic (in rank order of the strength of the relation). There was also a weak relationship between confidence and performance (p [less than] .1). Figure 1 shows examples of these relations for individual players.

In contrast, tense mood was not a significant predictor of performance. Within-person correlations between tense mood and performance showed that 19 of the players had a negative correlation (3 of which were p [less than] .05), but 10 had a positive correlation (1 of which was p [less than] .05). In other words, most players performed better when they were feeling less tense, but some performed better when they were feeling more tense. Figure 2 illustrates a negative and a positive relation between tense mood and performance for two players.

The previous regression analysis tested whether players' moods predicted concurrent changes in their performance. An additional analysis was conducted to test whether players' performances or the performance of their team predicted concurrent changes in players' moods. This test was accomplished by repeating the regression analysis (including the same control variables) but using the measure of general positive mood for each session of play as the dependent variable and the concurrent measures of personal and team performance (entered simultaneously) as the independent variables. Personal performance was a significant predictor of general positive mood (prior [R.sup.2] = .64, [R.sup.2] Change = .06, F(35, 58) = 11.00, p [less than] .01), but team performance was not (prior [R.sup.2] = .64, [R.sup.2] Change = .02, F(35, 58) = 2.73, n.s.). This means that players were in more positive moods during good personal performances, but not during good team performances.

Between-player relations between moods, subjective performance and objective performance

Mean scores for mood and subjective performance during batting or bowling were calculated for players who identified themselves as batters or bowlers. For batters, mean scores during batting were then correlated with each player's batting average for the match, batting difference score (i.e. difference from previous season's average) and mean subjective performance during batting. For bowlers, mean scores during bowling were correlated with each player's bowling average for the match, bowling difference score (i.e. difference from previous season's average) and mean subjective performance during bowling.

The results, which are presented in Table 3, show that batters who performed better were in a more positive mood. More specifically, batters who performed better were happier, more energetic, more enthusiastic, more focused and more confident during batting, but were not significantly less tense. Table 3 shows that bowlers who were in a more positive general mood during bowling rated their own performance more highly. More specifically, bowlers who were less tense, more energetic, more focused or more confident during bowling rated their own performance more highly. However, there were no significant correlations between mood and objective bowling performance.


Table 3 also shows the level of agreement between subjective and objective performance measures. Specifically, the subjective ratings were significantly correlated with objective batting performance, but not objective bowling performance. The correlation between energetic mood and batting difference score (i.e. match average less previous season's average) was significantly greater than that between energetic mood and batting average for the match (Z = 3.14, N = 17, p [less than] .05). However, the other correlations involving difference scores were not significantly different from those involving absolute scores.
Table 4. Paired t tests comparing cricketers' retrospective ratings
of their moods during their best and worst performances

                       Best            Worst
                    performance     performance
                     Mean (SD)       Mean (SD)       df       t

Positive mood       15.05 (2.05)    10.81 (2.34)     28     8.34(**)
Happy               15.10 (2.82)    10.00 (4.78)     28     5.54(**)
Tense               10.17 (4.47)    12.76 (3.97)     28    -2.46(*)
Energetic           15.38 (2.72)     9.59 (4.00)     28     7.05(**)
Enthused            15.14 (2.89)    11.28 (3.56)     28     5.10(**)
Focused             15.86 (2.56)    10.76 (3.67)     28     6.88(**)
Confident           15.07 (3.60)     9.24 (4.41)     28     5.83(**)

* p [less than] .05; ** p [less than] .01.

Mood before play as predictor of subsequent subjective performance

The previous analyses examined concurrent relations between mood and performance. A between-player analysis of prospective relations was also conducted to test whether mood before the start of play (which cannot be affected by concurrent performance) predicted performance during the first session of play. There were insufficient datapoints to test this proposition using a within-player analysis, so a between-players correlation analysis was used based on data from a single day of the match (day three was selected to maximize the sample size). For each mood variable, a correlation between mood before play and subjective performance in the session of play before lunch was calculated. Happy mood (r = .46, N = 19, p [less than] .05) and focus (r = .49, N = 19, p [less than] .05) were significantly correlated with subsequent performance. Energetic, tense, confident and enthusiastic moods (rs = .26, -.02, .20 and .29 respectively) were not significantly correlated with subsequent performance. There was a weak association between general positive mood and immediate subsequent performance (r = .40, n = 19, p [less than] .1).

Moods during best and worst performances

Paired t tests were conducted on each mood variable to compare the players' ratings of their moods during their best and worst performances. Table 4 shows that there were significant differences for all the mood variables. In general, players rated their moods as more positive during their best performances compared to their worst performances. Specifically, players rated themselves as happier, less tense, more energetic, more enthusiastic, more focused and more confident during their best performances. The difference was smallest for tense mood, and it was found that nine players rated themselves as more tense during their best performance. By way of comparison, a maximum of three players rated their moods as more negative on any of the other mood variables during best performance.


The main aim of the study was to determine whether relations between mood and performance could be found in the professional team sport of cricket. The results of the study suggest that professional cricketers do show reliable relations between their mood and performance during a competitive match. Indeed, players' general positive mood explained 8% of the remaining variance in changes in their subjective performance between sessions of play, after 55% of the variance had already been explained by control variables.

The strength of relations between different mood variables and performance were assessed by three methods: a within-players analysis of repeated mood and subjective performance measures during a match; a between-players analysis of aggregate measures of mood, subjective and objective performance for the whole match; and a comparison of players' recollection of their moods during best and worse performances. The three methods were in broad agreement in showing that players perform better when they are happy, focused, energetic, enthusiastic and confident.

The energy and focus variables were the most consistent significant predictors of performance. However, these variables were also highly intercorrelated so they may be indexing or affecting the same process. For example, feelings of lack of energy and lack of focus may be a reflection or a cause of reduced information processing capacity, and it may be this reduced capacity that detrimentally affects performance. The autobiography of an experienced cricketer provides an example of this type of contingent relation: '... I was too keyed up, too full of nervous tension, so that I didn't focus properly on the ball' (Reeve, 1996, p. 47). However, in relation to this quote, the one variable in the present study that failed to show a consistent association with performance was how tense a player was feeling.

Although the majority of players performed better when they were less tense, there were some players who performed better when they felt more tense. This concurs with Hanin & Syrja's (1995a) assertion that the same emotion can be facilitating or debilitating to performance depending on the individual. It is also consistent with Jones's (1995) notion that some performers perceive their anxiety as debilitative while others perceive it as facilitative. Hanin & Syrja (1995a) have also shown that the optimal level of precompetitive anxiety varies from person to person.

Studying mood-performance relations in sport

The present study has found reliable relations between mood and performance in the sport of cricket. This contrasts with the results of previous studies of long duration team sports, including cricket (see Terry & Youngs, 1996). The most plausible explanation for this discrepancy is that the present study used repeated measures of mood throughout competition, rather than just precompetitive mood. This method not only allowed an examination of mood-performance relations within players over time, but also enabled the mood data to be aggregated for cross-sectional analysis. Both analysis methods produced significant results. There are, however, practical difficulties in obtaining a sufficient number of response points from professional sports players during a competitive match. Indeed, most sports are of shorter duration than cricket, which means that the approach would be even more difficult to apply in other sports. An alternative approach for studying other sports might be to sample mood before and after a number of consecutive competitive matches.

Another feature of the current study was its use of subjective as well as objective performance measures. The objective measures included objective performance figures that were subjectively referenced by subtracting the players' performance average for the previous season from their current match performance average. These difference scores produced correlations with subjective performance and mood ratings of similar magnitude to the correlations that involved absolute match performance scores. A slightly worrying result from the study was that the subjective ratings of performance were sensitive to objective batting performance but not objective bowling performance. Bowlers' judgments of their own performance were not therefore based on their bowling average. A possible reason is that bowling average partly depends on the performance of the bowler's team-mates: for example, it can depend on whether team-mates have dropped easy catches or completed difficult catches. The condition of the pitch can also make a significant difference to bowling performance. Bowlers may therefore adjust their ratings to take account of these external influences when they judge their own performance.

With respect to the choice of mood measures, one point to note is that the results showed a very high intercorrelation between the energy and enthusiasm variables. The inclusion of the enthusiasm scale may therefore have been redundant. However, it is possible to imagine scenarios in which the two variables will differ, for example when a player is very tired but is still enthusiastic about a game.

Even though the present study examined mood as a predictor of changes in performance (by including a variable for previous performance in the regression models), the results do not necessarily show that mood influenced performance. Alternative causal explanations of the relations between mood and performance are possible. For example, the results show that performance predicted changes in general positive mood about as well as general positive mood predicted changes in performance. It seems likely that there is a bidirectional relationship between mood and performance. For example, a player might perform badly, feel bad as a consequence, and therefore play even worse. Another alternative, however, is that mood and performance changed in tandem because they were affected by the same internal or external factors. For example, concurrent changes in mood and performance could be the consequence of an internal change in mental resources or an external change in playing conditions. Recent research has also shown that the team's mood can influence players' moods and performances (Totterdell, 1999). However, in support of the proposition that personal mood influenced performance in the current study, there was some evidence that players' mood before play predicted their subsequent immediate performance.

Practical applications

The results of the study imply that the moods of players in professional team sports are partly responsible for variations in their performances. Coaches and players should therefore consider incorporating the development of mood control skills within players' training schedules. Recommendations already exist within sport psychology for improving skills relating to many of the mood-related variables used in the present study, such as focus, energy, confidence and tension (e.g. Howe, 1993; Syer & Connolly, 1984). This effort could be broadened to make use of research from other domains such as mental control and mood regulation (e.g. Morris, 1989; Parkinson et al., 1996; Wegner & Pennebaker, 1993). However, the effectiveness of intervention techniques based on this research would need to be systematically evaluated and then adapted to suit the needs and mood profiles of individual players.

The ability of professional cricketers to score runs can be viewed as a measure of their productivity at work. The results of this study might therefore also generalize to performance and productivity in other types of job, particularly if those jobs require skills that are inherent in cricket such as physical dexterity, exceptional eye - hand coordination, rapid decision-making and teamwork. However, further research is required to investigate whether the mood effects found in the present study generalize to performance in other work domains.

Finally, this study has indicated that the term 'mood score' could have a meaning in the domain of professional sport other than the measurement of mood. When the talent and technique of players is equal, the most effective sporting competitors may be those who can convert their moods into runs, goals, points and wins.


This work was supported by a grant from the Economic and Social Research Council, Swindon, England. The author would like to thank the cricket clubs, coaches, physiotherapists and players for their cooperation, and Des Leach for his help.


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Author:Totterdell, Peter
Publication:British Journal of Psychology
Date:Aug 1, 1999
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