Introduction
The Soccer Penalty Kick
Penalty-shoot-outs are a method to determine the winning team in soccer games that cannot end in a draw. If the match score remains tied after 90 min regular playing time as well as 2 x 15 min extra-time, then penalty-shoot-outs come into use. The teams are required to alternately perform five free shots, at a distance of 11 m from the goal in the presence of the opponent’s goalkeeper, known as penalty kicks. Each player is allowed one attempt and the team with the greatest score wins. If necessary this process is repeated multiple times (The Football Association, 2019). Succeeding in penalty-shoot-outs is therefor often referred to as “winning the lottery” as the outcome seems to depend on luck rather than skills. In fact around 25% of penalty kicks are not converted (Mc Garry and Franks, 2000). According to Franks and Hanvey (1997) shots to distal areas of the goal should physically be impossible to save and also a study of Bar-Eli and Azar (2009) claims a 0% save rate for shots toward the upper third of the goal, yet 60-70% of penalty kicks are centrally placed. The literature review of Wood et al. (2015) underpins the importance of shooting accuracy toward “optimal areas” of the goal and effective aiming technique to minimise the role of luck. The paper suggests that luck may only be significant for less accurate and centralised shots.
Performing under pressure
Despite the relatively high failure rate (25%) mentioned above in penalty takers, an comparatively higher failure rate applies to the goalkeeper. The statistical advantage given to the penalty taker in a shootout scenario may increases the pressure to convert penalty kicks (Jordet, 2009; Wood et al., 2015). Both pressure to perform as well as competitive concerns are known stressors that can lead to state anxiety (Dunn and Syrotuik, 2003) but does not necessarily link to decreased sport performance (Weinberg and Gould, 2015). Even though the review of Rocha and Osório’s (2018) on competitive anxiety defines sports competitions as a source of threat per definition ‘ … as the athlete's image is usually associated with his or her performance, the final result is always uncertain, there is exposure to public opinion and judgement by third parties, among others’, the paper also links this condition to enhanced sports performance. Similarly, Ruiz et al. (2017) refer to enhanced performance at high levels of state anxiety in some athletes, however the paper also claims that the individual zone of optimal function (IZOF) varies in every individual and that performance may also decrease when getting over-anxious. Alongside somatic anxiety (physical response or arousal), anxiety in sport settings is also characterised by cognitive anxiety (nervousness, worry and apprehension). State anxiety in this regard is specified as a temporary and constantly changing mood component, whereas trait anxiety is part of the personality that influences behaviour (Spielberger, 1966).
Worrying is a known cognitive state anxiety component that can result in decreased performance effectiveness and efficiency (Horikawa and Yagi, 2012). The study of Jordet (2009) identified worrying about the goalkeepers performance as the greatest source of threat for penalty takers and confirms that individuals perception of control may alter under pressure. Reeves et al. (2007) describes this phenomenon as choking under pressure and refers to ‘… sub-optimal performance despite individual motivation and situational demands for superior performance …’. Distraction theory in this regard additionally provides evidence for this observation and claims that pressure creates a dual task-scenario and therefore reduces ability to execute a learned task, by trying to split attentional focus between executing the motor task and worrying about the outcome of the performance (Lewis and Linder, 1997). A second, though opposing, theory that emerges in the literature in this context, is the explicit-monitoring theory. Compared to the distraction theory that ultimately requires undivided attention on motor control, the explicit-monitoring theory argues that internal skill-focused attention interferes with automation in learned skills (Baumeister, 1984; Lewis and Linder, 1997; Beilock and Carr, 2001) known as the ‘reinvestment effect’ (Masters, 1992).
Reinvestment during performance
In order to generate a greater understanding of movement details, novice tend to experiment with task execution during early stages of learning (Fitts and Posner, 1979). With increasing improvement, learning progresses from a verbally encoded, ‘declarative knowledge stage in which performance is consciously controlled and requires much attention, to a procedural knowledge stage in which performance is automatic and requires little attention’ (Masters and Maxwell, 2008:online). Once motor-skills are automated, consciously thinking about executing motor skills result in decreased performance (Baumeister, 1984). A drop in performance occurred in every case: soccer (Ford et al., 2005), hockey dribbling (Jackson et al., 2006), golf putting tasks (Beilock and Carr, 2001) and trampoline jumps (Hardy et al., 2001); where skilled performers followed task-relevant declarative cues. While internal focus of attention encourages conscious movement control (Wulf et al., 1999; Wulf et al., 2001; Wulf et al., 2007) and worsen performance, an external focus of attention on the other hand seems to be beneficial in automated skills (Wulf et al., 2001). ‘It is almost as if giving the declarative system of experts something to keep it busy makes it easier for the procedural system to get down to business’ (Allard and Burnett, 1985:online). Baseball hitters in the mentioned study performed considerably greater when asked to keep an eye on external factors like the colour or location of pitcher’s arm-ribbons. So, while novice tend to benefit from an internal attention focus, as they rely on declarative knowledge (Masters and Maxwell, 2008), experienced athletes show decreased performance. However, recommendations in literature are directed against larger amount of declarative knowledge during motor learning process, to reduce accessibility under pressure, especially in high-reinvesters (Masters, 1992; Maxwell et al., 2000). The paper of Ford et al. (2005) points out the necessity of motor-skills to be at least partly automated for reinvestment to show disruptive effects, yet there is uncertainty at which stage of learning reinvestment starts showing negative effects on performance. Hanin (2000) reported psychological pressure to be the most apparent trigger resulting in reinvestment. As a strategy to maintain or improve performance under high pressure, high-reinvesters cope by shifting attention toward an internal attention focus and conscious execution of automated motor-skills, that fail to be successfully performed (Master, 1992; Master et al., 1993). This regression can even go that far that ‘…high level performance can regress to early stages of skill development …’ (Masters and Maxwell, 2008:online).
In 1993, Masters et al. accounted personality differences to be responsible for attempts in consciously controlled skill execution and also named psychological, physiological and environmental factors as potential causes. Masters (1992) describes reinvestment as a result of the progression-regression process (Fitts and Posner, 1979) that occurs under pressure. In order to identify potential high-reinvesters Masters et al. (1993) developed a scale, which he adapted in 2005 more movement specific. The Movement Specific Reinvestment Scale (MSRS) consist of 10 items equally covering 1) movement self-consciousness and 2) conscious motor processing components, providing a separate score for each. Participants self-report on a six-point Likert scale from 1 (‘strongly disagree’) to 6 (‘strongly agree’) in regard of how they move in their sport. The Movement self-consciousness block refer to statements like ‘I am concerned about what people think about me when I am moving’ and conscious motor processing section include items like ‘I reflect about my movement a lot’. A study of Bawden et al. (2001) urged that performers with a greater self-consciousness score were more likely to be affected by self-focused attention compared to participants with low self-consciousness scores. Also Masters and Maxwell’s (2008) review on reinvestment confirmed that pressure manipulations as well as self-focus attention lead to decreased performance in high-reinvesters throughout the majority of experiments in this field. In order to consciously control motor skills, relevant cues are recalled from the memory storage (Williams et al., 2004). Utilisation of this cues then takes place in the working memory and therefore lower its working capacity (Poolton et al., 2005; Baddeley, 2007). The paper of Eysenck et al. (2007) mentions two systems in regard of attentional control theory (ACT) that govern human behaviour: 1) The top-down system, lead by knowledge, expectations and goals contents from the working memory and 2) the bottom-up system, searching for salient stimuli and potential threats in the environment. Pressure and anxiety will favour the bottom-up system due to the need for ‘…rapid reactions to escape any potential negative consequences for one’s well-being’ (Eysenck et al., 2007:online). The bottom-up system have shown to also influence gaze behaviour, as anxious penalty shooters tend to fixate the goalkeeper, the threatening and distracting stimuli, especially when moving around, and kick significantly closer to the goalkeeper (Wilson et al., 2009). So, if the working memory is occupied under pressure utilising task relevant declarative knowledge cues from the memory storage to cope with anxiety, the top-down system is lacking access to the working memory to get goal-driven content. Aiming behaviour will then be influenced and eventually dominated by the bottom-up system, looking for the source of threat and performance failure are likely to occur in high-reinvesters with a great self-conscious score when executing automated skills.
Aims and objectives
The purpose of this study was to investigate the effects of anxiety and how performance (accuracy and penalty kick rate) may alter under high pressure vs. low pressure in high-reinvesters compared to low-reinvesters. The one tailed hypothesis was that high reinvesters will show decreased performance scores in accuracy and penalty kick conversion under high pressure compered to low-reinvesters.
Methods
Participants
30 male soccer player between 25 years (SD ±3.2) with a minimum of 3 years professional experience on club level and a 31-year old goal keeper with 17 years of experience on club level. Participants must not attend if recently injured and back on the pitch less than 6 months or have less than 3 years professional experience on club level. Players performed Masters ‘The movement specific reinvestment scale’ questionnaire, rating themselves on a six-point Likert Scale ranging from ‘strongly disagree’ to ‘strongly agree’ and were split into high- (n = 16) and low-reinvesters (n = 14) groups. Participants with total MSRS scores of < 28 were assigned to the low-reinvesters group and participants with scores > 28 were transferred to the high-reinvesters (Chu and Wong, 2019). Total MSRS scores in low-reinvesters had a mean of 18 and 50 in high-reinvesters (SD ∼4 in both groups). All participants provided informed consent.
Apparatus
A size 5 soccer ball was shot from the standard penalty kick distance of 11 m. The goal was a full size 7.32 m x 2.44 m soccer goal marked on an enclosed wall. 32 mm thick gym mats covered the full width of the goal to minimise injury risks and allow the goalkeeper to dive for the ball. A Mobile Eye Tracker System including a recording device worn in a pouch around the waist (Applied Science Labora- tories, Bedford, MA) measured at 25 Hz.
The tracking system was paired with a Dell Inspiron 6400 laptop, using “Eye- Vision” recording software. A scaling factor was placed in the scene view of the eye tracker (Wood, 2017).
Procedure
Testing was carried out over two sessions. The first session groups received a short briefing on setup and procedure and were tested separately. Initially Krane’s (1994) Mental Readiness Form-3 (MRF-3) was applied 5 minutes prior to testing to measure state anxiety. Compared to the common Competitive State Anxiety Inventory-2 (CSAI-2), the MRF-3 is a shorter alternative with correlations between 0.68-0.76 and the method has already been used in other sport settings measuring ACT, for example Wilson et al. (2009) and Wood et al. (2017). Participants were asked to only rate their cognitive anxiety, on a bipolar 11-point Likert scale. Somatic anxiety and self-confidence measures were not considered. After getting familiarised to the apparatus and testing environment, players started with 10 trial shots before executing a set of 10 soccer penalty kick each in a low anxious setting. Similar to a real competition penalty takers were asked to score as many kicks as they can with the aim to collect as many points as possible for the team. In order to create a high anxious scenario a competitive penalty kick shootout between the two groups was set two weeks after under the same setting. An experienced goal keeper was added in both scenarios and asked to save as many shots as possible. To prevent the goal keeper from fatigue a 10-min break was taken between participants.
Measures
Cognitive anxiety scores of the MRF were measured before each testing session. The number of penalty kicks converted out of 10 were added for each attempt. Accuracy was measured in centimetres, examining the centre of the ball away from the lateral post of thee goal when hitting the target wall. Saved shots were given an accuracy score approximate to were they would have hit the goal and approximate to were they would have hit the goal and missed shots were not given any accuracy scores at all (Wood et al., 2017). The eye tracker video file specified accuracy via frame-by-frame analysis with a precision of 5 cm using Quiet Eye Solutions analysis software.
Data analysis
The data was analysed running through a 2 (scenario: no-pressure vs. pressure) x 2 (groups: low- vs. high-reinvesters) mixed method ANOVAs, each for 1) Anxiety x Groups, 2) Scores x Groups and 3) Accuracy x Groups using SPSS. Significant interaction effects were followed up with pairwise t-tests.
Results
Anxiety. A significant main effect was found in anxiety within groups F(1,28) = 155.79, p < 0.01. Both high-reinvesters as well as low-reinvesters more than doubled anxiety scores when under pressure p= < 0.01. Between measurement did not show significant effects F(1,28) = 3.14, p = 0.09. The pairwise comparison revealed that low-reinvesters had slightly but not significant (p = 0.06) higher anxiety scores under pressure.
Scores. There were no significant main effect in scoring performance within groups F(1,28) = 3.51, p = 0.07, nor between groups F(1,28) = 0.17, p = 0.69. High reinvesters did have a 10% drop in performance under pressure, but non significant p = 0.06.
Accuracy. Significant main effects occur in accuracy scores (Figure 1) within groups F(1,28) = 6.68, p = 0.02 and revealed significant interaction F(1,28) = 7.34, p= 0.01 between accuracy and groups as well. In fact high-reinvesters significantly hit the ball closer to the goal keeper under pressure 114.43 cm, SD 33.46 (p < 0.01) while low-reinvesters showed stable results 92.79 cm, SD 22.98 (p = 0.93) compared to no-pressure. The pairwise comparison of high-reinvesters vs low-reinvesters showed significant differences p= 0.05 under pressure.
Figure 1: Accuracy differences (cm) under 1) low pressure and 2) high pressure condition
Discussion
Both low-reinvesters as well as high-reinvesters have greater anxiety scores under high pressure compared to low pressure manipulations. Even though penalty kick conversion rate dropped by 10% in high-reinvesters under high pressure, changes did not appear to be significant compared to low-reinvesters in this study. The hypothesis, that high-reinvesters show decreased performance in penalty kick accuracy compered to low-reinvesters can be proven true.
High-reinvesters have greater cognitive anxiety scores under high pressure compared to low pressure, but so have the low-reinvesters. Greater anxiety does not necessarily mean that high-reinvesters perform less accurate due to increased cognitive state anxiety, the same way it does not say that low-reinvesters perform better than high-reinvesters because of sufficient cognitive activation either. It says exactly what the literature says: human behaviour is complex. Competitive concerns are a stressor for state anxiety (Dunn and Syrotuik, 2003) and the results in this study confirm that state anxiety does increase prior to competition in both low- and high-reinvesters. But what does this mean in terms of performance? The effects of anxiety on performance are controversially discussed in the literature. While high level of state anxiety does not necessarily affect sport performance negatively (Weinberg and Gould, 2015) and can be linked to enhanced performance (Rocha and Osório, 2018), other papers like Ruiz et al. (2017) underpin that the IZOF may vary in individuals and that drops in performance may occur in over-anxious athletes. In conclusion there is consent among scientist about the negative effects of pressure and anxiety on motor skill performance (Baumeister, 1984; Masters, 1992; Masters et al., 1993; Lewis and Linder, 1997; Beilock and Carr, 2001; Dunn and Syrotuik, 2003; Poolton et al., 2005; Reeves et al., 2007; Baddeley, 2007; Ruiz et al., 2017; Rocha and Osório, 2018). However, high-reinvesters significantly (p < 0.01) failed to maintain accuracy performance when anxious and this study can provide evidence for the hypothesis that high-reinvesters show significant decline in accuracy performance under high pressure. This finding is pretty common in the literature (Master et al., 1993; Jackson et al., 2006; Masters and Maxwell, 2008). A study of Wilson et al. (2009:onilne) that examined gaze behaviour in heterogenous groups confirmed that ‘shots in the high-threat condition were placed significantly closer to the centre of the goal (nearer the goalkeeper) than those in the low-threat condition.’ The fact, that low-reinvesters maintained accuracy levels at 93% under high pressure in this study, assumes that pressure responses may be due to personality differences (Masters et al., 1993), rather than anxiety per se. It seems as if high-reinvesters are at greater risk to be affected by anxiety and pressure (Bawden et al., 2001), for they tend to modify attention under high pressure as a strategy to maintain efficiency of performance, towards an internal attention focus (Masters, 1992; Masters et al., 1993). Efficiency describes ‘… the relationship between the effectiveness of performance and the effort or resources spent in task performance …’ (Eysenck et al., 2007:online). Bawden et al. (2001) assumes that high-reinvesters, in particular with a greater MSRS self-consciousness scores, were even more likely to be affected by self-focused attention. Yet it remains unclear to what extend low-reinvesters may be affected by high self-consciousness scores. Internal skill-focused attention interferes with automation in learned skills (Baumeister, 1984; Lewis and Linder, 1997; Beilock and Carr, 2001; Ford et al., 2005) and encourages conscious movement control (Wulf et al., 1999; Wulf et al., 2001; Wulf et al., 2007). Consciously accessing the execution of motor skill correctly, results in performance decrements (Baumeister, 1984). The more effort is invested to reach a certain performance level, the greater efficiency will decrease (Eysenck et al., 2007). Automated motor skills run off better with an external focus of attention (Wulf et al., 2001) and high-reinvesters that cope with an internal attention focus under high pressure cause a chain reaction that result in declined accuracy performance. The first out of three Dominos drops when internal attention focus stimulate conscious motor control. 1) Conscious motor control demands on task relevant cues from the memory storage (Williams et al., 2004), that are further processed in the working memory and lowering its capacity (Poolton et al., 2005; Baddeley, 2007). 2) The top-down system in regard of ACT can not be led by goal contents from the working memory as remaining capacities can not provide sufficient inputs and therefore favours the bottom-up system (Eysenck et al., 2007). 3) The bottom-up system is the second out of two systems that governs human behaviour and is driven by salient stimuli and potential threats in the environment. In search of danger, gaze behaviour starts to shift towards the goalkeeper, the source of threat, and penalty takers aim significantly further away from optimal areas of the goal and significantly closer towards the centre of the goal, especially when the goalkeeper is distractingly moving around (Wilson et al., 2009). Psychological pressure is the main trigger that leads to internal focus and less accurate shots (Hanin, 2000) and therefore have greater chances to negatively impact accuracy performance in high-reinvesters as confirmed in this study.
Even though high-reinvesters show significant decline in accuracy performance in this study when under high pressure, conversion rates were not affected by this maladaptations significantly. Therefore the hypothesis that high-reinvesters show significant drops in score performance under high pressure can be declined in this study. An explanation for the non-significance of changes in hit-rate may be that anxiety related differences in terms of results simply do not impact high-reinvesters at certain proficiency level (Jones et al., 1994), due to the great competitive barriers players have to master to even get to this stage. Wood and Wilson (2010:online) mentioned ‘… greater resistance to the negative effects of threat and/or distraction …’ in elite players and also Furley et al. (2017:online) questioned generalisation of laboratory findings ‘… due to the complex interplay of numerous variables affecting performance in association football penalty shootouts’. Wood and Wilson (2010) additionally state that actual penalty shootouts are significantly more threatening than test-settings and referred to the fact that in a real shootout only one crucial attempt is available to convert a penalty kick, compared to a series of shots in the laboratory. An other explanation for non-significant drop of conversion-rate can also be some limitations of this study: 1) Perhaps the threat of the shootout scenario was not great enough to actually have a significant impact on the performance outcome. A paper of Wilson et al. (2009) used a combination of multiple treats and ego-threats to ensure high level of pressure. More life like threats like audience and score tables (Rocha and Osório, 2018), ban from upcoming competitions as well as desirable prize-money may increase the pressure to perform in testing environments and show more significant differences. Also instructing the goalkeeper to distract penally takers by moving around as well as rewards for every saved shoot can lead to increased goal keeper performance Wilson et al. (2009) and greater worries in penalty takers about the outcome. Masters et al. (1993) also mentions environmental factors to be jointly responsible for reinvestment. 2) Further, participants were not explicitly told to aim for optimal areas of the goal. Performance drops may have had occurred differently, when trying to aim for optimal areas of the goal. The paper of Wood and Wilson (2011:online) suggested that `… by aligning gaze with aiming intention, penalty takers could increase their shooting accuracy …’ and had 50% less shots been saved by the goalkeeper. Explicitly instructing high-reinvesters to shoot toward optimal areas of the goal may have two concurring outcomes under pressure. In regard of ACT aiming for specific spots of the goal could either a) count as an external factor, prevent self-focus (Wulf et al., 2001) and enhance performance in high-reinvesters or b) expectations to aim on specific targets of the goal will increase the pressure to perform even more and favour the bottom-up system Masters and Maxwell (2008). Aiming behaviour will be anxiety driven in the bottom-up system and lead to performance bias in automated skills (Wilson et al., 2009). 3) Hardy and Fazey's (1987) chaos theory that explains choking under pressure, indicates that changes in performance dependent on the interaction of physiological arousal and cognitive anxiety. Even though unlikely, the paper claims that greater levels of cognitive anxiety have less impact on the performance when physiological arousal is low. The catastrophic effect occurs under high levels of somatic and cognitive anxiety, suggesting that physiological arousal is not necessarily crucial for performance but determine for choking under pressure when cognitive anxiety increases. Other measurement techniques to examine more objective data like electrical activity in the cortex (EEG) to access cognitive response or heart rate, blood pressure and hormone levels to measure somatic changes (Lacey 1967) have leaked practicability on the pitch and therefore do not appear in the literature. Differences in this regard may be more valid to compare with each other and may provide deeper explanations of the observed effects. Testing this objective measures under physical fatigue, for example after a prolonged game, will create even more life-like conditions and maybe cause greater physical arousal, that either will provoke the chaos theory or explain some of the performance declines in high-reinvesters. Masters et al. (1993) additionally mentions to environmental components, also physiological factors to be jointly responsible for reinvestment. Other papers agree and recommend further research of the anxiety-attention relationship in far-aiming skills and suggest to ‘…combine objective measures of reinvestment with gaze behavior [sic] measures…’ Payne et al. (2019:online).
Nevertheless coaches may also want to acknowledge the individual zone of optimal function in high-reinvesters (Ruiz et al., 2017). The IZOF describes the state anxiety - performance relationship and provides individual reference points and criteria of pre-competitive state anxiety (Hanin, 2000), state anxiety scores within IZOF are expected to favour performance outcome. Results can easily be compared to individual scores at any time and coaches can carry out interventions that either decrease or increase anxiety levels of high-reinvesters prior to competition (Weinberg and Gould, 2015). As Wood et al. (2015) mentioned, luck may only be significant for less accurate and centralised shots, pointing out the importance of shooting accuracy toward “optimal areas” of the goal. Wood and his colleagues also recommend effective aiming techniques to minimise the role of luck as shots to distal areas of the goal should physically be impossible to save (Franks and Hanvey, 1997; Bar-Eli and Azar, 2009). High-reinvesters may want to improve aiming behaviour to avoid declines in accuracy performance when under high pressure. In this regard Wood and Wilson (2011) confirmed the Quiet-Eye Training (QET) as an effective method to improve gaze control under pressure. Far aiming skills benefit from visual attention and enhance coping abilities under pressure. Vickers (1996) developed the QET program to improve aiming behaviour in far aiming skills. Later final fixation to the target in QET trained participants ‘allows performers an extended duration of motor programming, while minimizing [sic] distraction from other environmental cues’ (Wood and Wilson, 2012:online), considering that quicker fixations may lead to decision making bias.
An interesting paper in regard of decision making that can be useful for further studies is a study of Kinrade et al. (2015), measuring reinvestment and decision making under pressure in basketball players. The paper examined the validity of the Decision Specific Reinvestment Scale (DSRS) and came to the conclusion that higher ‘scores were associated with a speeding of performance from the low to high pressure condition in the psychomotor tasks’ and ‘were significantly correlated with performance decrements’ (Kinrade et al., 2015:online). The DSRS is similarly like the MSRS an edited version of the original Reinvestment Scale and comprises decision reinvestment and decision rumination factors. Kinrade et el. (2010) developed the DSRS to measure the propensity for reinvesting explicit knowledge in decision-making tasks. A study of Jackson et al. (2013) that examined the validity of the original
Reinvestment Scale, the MSRS and the DSRS claims, DSRS to be an even greater predictor of accuracy changes under pressure than the MSRS. Gaze control may reverse the process of an inward attention focus and promote the top-down system by giving it an external attention focus to deal with (Wulf et al., 2001).
Further studies in the field of reinvestment in soccer penalty kicks may want to extend the literature by including gaze behaviour measurement and DSRS scores as well. For further studies it may also be interesting to study the degree to which low-reinvesters are generally aligned with their IZOF compared to high-reinvesters and then compare under pressure. A hypothesis may be that low-reinvesters perform better under pressure because they are in better alignment with their IZOF than high-reinvesters and therefore have greater self-confidence and interpret their anxiety levels more positive (Jones and Swain, 1995). An even more far-fetched hypothesis may be that high-reinvesters are taught to function at certain level of cognitive and somatic anxiety, as part of larger amount of declarative knowledge during motor learning (Masters, 1992; Maxwell et al., 2000), that is outside their IZOF (Hanin, 2000) and therefore regress performance under pressure to early stages of learning (Fitts and Posner, 1979) and never outgrow learning phase to actually develop the required self-confidence (Jones and Swain, 1995) about their own skills and abilities to successfully execute them under pressure compared to low-reinvesters. A reason for this may be that sport psychology is neglected till athletes reach elite level and some coaches seem to have their own view of how an athlete has to function to perform. Additionally splitting groups by MSRS self-consciousness scores only may be a cue for greater significant differences, as athletes with greater self-consciousness score are more likely to be affected by self-focused attention (Bawden et al., 2001). Perhaps changing pressure manipulations from low to absolute no pressure in first attempt, for example in absence of a goalkeeper, as well as aiming for specific targets on the goal may provide greater evidence for differences between low- and high-reinvesters.
Summery
In conclusion the study can confirm significant decline in accuracy performance under pressure in high-reinvesters and more centralised shots towards goalkeeper 114.43 cm (distance from pole), SD 33.46 (p < 0.01) while low-reinvesters showed stable results 92.79 cm, SD 22.98 (p = 0.93). A 10% performance drop in terms of penalty kicks converted could be observed under pressure in high-reinvesters but provided non-significant differences (p= 0.06) when compared to low-reinvesters. The significant decrease in accuracy performance in high-reinvesters did not lead to expected drops of conversion rate, although a slightly greater slope down of performance could be observed under pressure in high-reinvesters. A reason for non-significant differences in penalty scores may be that pressure manipulations in this study were not sufficient enough to actually have an effect on high-reinvesters’ performance. Also lack of explicit instructions to aim for optimal areas of the goal can be an explanation for non-significance of score differences. Further, competitive concerns can be confirmed as a stressor for state anxiety. MRF scores for cognitive anxiety in both low- and high-reinvesters more than doubled under pressure F(1,28) = 155.79, p < 0.01. Examining groups by MSRS self-consciousness score may greater reinvestment effects. Psychological pressure can be confirmed as the main trigger for anxiety and reinvestment, but raise in physiological arousal may be determine for performance bias and physiological manipulations need to be included in future study designs.
Acknowledgment
The author would like to thank Greg Wood for his constructive comments on the development of this study and on earlier drafts of this paper.