
==== Front
Eur J Sport Sci
Eur J Sport Sci
10.1002/(ISSN)1536-7290
EJSC
European Journal of Sport Science
1746-1391
1536-7290
John Wiley and Sons Inc. Hoboken

39079750
10.1002/ejsc.12171
EJSC12171
Original Paper
ORIGINAL PAPER
Applied Sport Science
Relation of general‐perceptual cognitive abilities and sport‐specific performance of young competitive soccer players
Schumacher Nils https://orcid.org/0000-0003-0188-0962
1 nils.schumacher@uni-hamburg.de

Zaar Christoph 1
Kovar Jannik 1
Lahmann‐Lammert Lorenz 1
Wollesen Bettina 1
1 Faculty of Psychology and Human Movement Institute of Human Movement Science University of Hamburg Hamburg Germany
* Correspondence
Nils Schumacher, Faculty of Psychology and Human Movement, Institute of Human Movement Science, University of Hamburg, Hamburg, Germany.
Email: nils.schumacher@uni-hamburg.de

30 7 2024
9 2024
24 9 10.1002/ejsc.v24.9 12701277
15 5 2024
18 8 2023
10 7 2024
© 2024 The Author(s). European Journal of Sport Science published by Wiley‐VCH GmbH on behalf of European College of Sport Science.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

In soccer, skill is significantly influenced by motor skills and physical constitution. In addition, perceptual‐cognitive abilities are thought to affect sport‐specific performance. Nevertheless, there are hardly any studies investigating the relationship of general cognitive abilities with sport‐specific performance. Therefore, the aim of this study was to analyze relationships between general perceptual‐cognitive abilities and soccer‐specific performance in competitive youth sports. Thirty highly talented male youth soccer players aged 12–14 years completed various perceptual‐cognitive (selective attention, cognitive flexibility, inhibition, working memory, peripheral perception, and choice response) and sport‐specific on‐field tests. Cognitive abilities were assessed using a computer‐based test system. Soccer‐specific performance skills were evaluated by two sport‐specific on‐field tests. The relation between perceptual‐cognitive abilities and soccer‐specific performance was examined using a correlation analysis as well as a four‐stage regression analysis. Overall, the expression of general perceptual‐cognitive abilities was found to have an impact on performance in soccer‐specific test situations, particularly cognitive flexibility and selective attention. Our results suggest that general cognitive tests could be an important tool for the evaluation of cognitive abilities in soccer. This study brings together key approaches in expertise research and makes a significant contribution to a better understanding of expertise in soccer.

Highlights

This study is one of the few to examine the relationship between general cognitive abilities and sport‐specific performance.

The results highlight the relevance of general perceptual‐cognitive abilities for soccer‐specific performance.

This study provides a link between different theories of expertise research.

cognition
on‐field test
perceptual‐cognitive abilities
soccer
source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:03.09.2024
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pmc1 INTRODUCTION

Talent identification and development in youth competitive soccer requires a comprehensive understanding of athletic expertise. In addition to an outstanding physical constitution, in team sports, such as soccer, expertise is significantly influenced by motor and perceptual‐cognitive skills (Fuster et al., 2021; Williams & Ericsson, 2005). Nevertheless, the relationship between general performance‐related cognitive abilities and sport‐specific performance has not been sufficiently clarified.

Some cognitive abilities seem to be relevant for sport‐specific performance. Soccer players must perceive situations, process stimuli, make the right decision as quickly as possible, and execute the action at the right moment (Baker et al., 2003). Hence, for expertise and successful performance in soccer, executive functions (EFs), visual perception, and reaction abilities are of special importance and are linked to neural efficiency (Li & Smith, 2021).

Working memory, inhibition, and cognitive flexibility constitute the core EFs (Diamond, 2013). EFs are assumed to be required for decision‐making processes in soccer (Vestberg et al., 2020). Besides, they are involved in the control and regulation of “lower level” non‐EF cognitive processes such as reaction time (Huijgen et al., 2015).

In the context of the expert–novice paradigm, the relationship between expertise and perceptual‐cognitive abilities has been investigated in various studies. Within the cognitive‐component‐skill approach, it was shown that experts demonstrate better general perceptual‐cognitive abilities compared to novices (Vestberg et al., 2017; Voss et al., 2010). While in sport‐specific tests, differences were found between experts and novices in skills such as anticipation (Reilly et al., 2010); in general cognitive tasks, differences were found in some “core EFs” such as attention (Heppe et al., 2016), cognitive flexibility, inhibition (Huijgen et al., 2015; Vestberg et al., 2017), and working memory (Vestberg et al., 2017). Furthermore, it has been shown that experienced football players, among others, show better reaction times in general reaction time tests for both stimuli, perception in the periphery (Ando et al., 2001; Zwierko, 2007) and in the central visual field (Ando et al., 2001). Thus, these studies suggest a relation between sport‐specific performance and core EFs.

Nevertheless, only a few studies have investigated the relationship between sport‐specific motor skills, measured by on‐field tests, and general perceptual‐cognitive abilities. Heisler et al. (2023) demonstrated a relationship between EFs and sport‐specific decision‐making in soccer, particularly for working memory. Scharfen and Memmert (2019) showed that a larger attention window correlates positively with demanding motor skills such as dribbling in soccer. In addition, a positive correlation was found between working memory and dribbling, ball control, and ball juggling. However, especially for 13‐, 14‐, and 15‐year‐olds, evidence on the relationship between perceptual‐cognitive abilities, such as attention, cognitive flexibility, working memory, peripheral perception, choice response, and sport‐specific motor skills, measured in on‐field tests is weak so far. This gap should be closed as accelerated maturation of EFs takes place at that age (Schumacher et al., 2018; Wollesen et al., 2022) and might represent an important time for talent development in youth soccer (Huijgen et al., 2015).

Furthermore, objective and time‐efficient computer‐based test systems, such as the Vienna Test System (VTS) (Ong, 2015), might be of great advantage in practical application to soccer if they could also reliably predict performance on the soccer field.

However, there are still certain challenges to reliably measure the on‐field performance in soccer. Memmert (2004) suggests different applied game test situations in order to investigate potentially related cognitive abilities. According to Memmert's considerations, game situations can make perceptual cognitive abilities in basic tactical tasks assessable.

The aim of the current study was to analyze relationships between general perceptual‐cognitive abilities (selective attention, cognitive flexibility, inhibition, working memory, peripheral perception, and choice response) and soccer‐specific performance in youth competitive soccer players. We hypothesize a relationship between youth soccer performance and their general cognitive abilities.

2 METHOD

2.1 Participants

A total sample of N = 30 highly talented U13 and U14 male youth soccer players (mean age 13.3 ± 0.76 years, age range 12–14 years, and training experience 7.2 ± 1.5 years [Table 1]) of a German soccer club (highest regional soccer league for their age classes) were recruited.

TABLE 1 Descriptive characteristics of the study participants.

	GK (n = 3)	DEF (n = 9)	MF (n = 11)	ST (n = 7)	Total (N = 30)	
Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD	
Age (years)	13.67 ± 0.58	13.33 ± 1.0	13.36 ± 0.81	13.14 ± 0.38	13.33 ± 0.76	
Height (cm)	173.67 ± 8.02	171.11 ± 10.11	165.91 ± 6.12	166.29 ± 10.19	168.33 ± 8.70	
Weight (kg)	66.33 ± 10.97	56.56 ± 9.03	53.91 ± 8.34	54.57 ± 8.4	56.10 ± 9.09	
BMI (kg/m2)	21.93 ± 2.82	19.19 ± 1.41	19.49 ± 1.93	19.62 ± 1.37	19.67 ± 1.84	
Soccer experience (years)	7.67 ± 1.53	7.11 ± 1.45	7.36 ± 1.69	6.71 ± 1.6	7.17 ± 1.53	
Abbreviations: cm, centimeter; DEF, defender; GK, goalkeeper; kg, kilogram; m2, square meter; MF, midfielder; ST, striker.

The sample included n = 3 goalkeepers, n = 9 defenders, n = 11 midfielders, and n = 7 strikers. 86.7% of the participants were right‐footed and 96.7% were right‐handed.

2.2 Measures

2.2.1 General perceptual‐cognitive abilities

Several general perceptual‐cognitive abilities were assessed using the computer‐based VTS (Schuhfried, 2016) as follows.

The reaction test (RT) was used to measure reaction time and motor time. The participants had to react as quickly as possible to a visual (yellow dot on black screen) and acoustic stimulus (“beep”). When a yellow dot appeared on the screen and the beep sounded simultaneously, the subject had to release a rest button and press a corresponding response button as quickly as possible. In the instruction phase, the subjects received feedback when they responded to the correct stimuli. No feedback was given in the test phase.

Using the determination test (DT), selective attention, reactive resilience, and reaction time were assessed. Participants reacted to different visual and auditory stimuli. The response was made by pressing the corresponding keys on the subject's keyboard and on two foot pedals. The stimulus presentation was adaptive, in that the target speed adjusted to the subject's performance level.

Visual working memory was measured by the n‐back test (NBN). The subject's task was to indicate whether the figure currently shown was congruent with the figure shown two frames before the current frame (2‐back paradigm). If the current figure was the same as the figure shown two pictures before, the subject was asked to react as quickly as possible by pressing a specific button.

The trail‐making test (TMT part A and B) (Llinàs‐Reglà et al., 2017) was used to assess cognitive flexibility, working memory, and attention. In part A, the task was to click on the numbers 1–25, which were randomly positioned on the screen, as quickly as possible one after the other using the mouse. In part B, the test subject marked the numbers 1–13 and the letters A–L as quickly as possible and in ascending order in the same way.

Peripheral perception was measured using the peripheral perception test (PP). The test consisted of a central tracking task on the screen (tracking a moving ball with a cursor) and a peripheral perception task. Light diodes were used to generate light stimuli that moved at a predefined speed in the peripheral visual field. Critical stimuli appeared at preset intervals, to which the test subject reacted by pressing a foot pedal. At the same time, the tracking task was processed on the screen. A detailed description can be found in earlier publications (Schumacher et al., 2020).

2.2.2 Sport‐specific on‐field tests

In order to assess the sport‐specific skills, two standardized sport‐specific tests were conducted on the playing field following Memmert's game test situations (Memmert, 2004).

In test 1 “Approach of the Ball to the Target” (ABT, Figure 1), the subjects were to transport the ball over a target line in a game situation (4 against 2). In the starting position, the attackers were on the starting line (white circles in Figure 1) and the defenders were at the height of the center line (black circles in Figure 1). Dribbling or running with the ball was not allowed. Each player had a total of 12 attacking attempts. After four attempts, the player constellation of the attacking and defending team was switched (rotating counterclockwise). This resulted in three sequences with four attacking attempts for each player. The game situations were conducted without offside rules. The match sequences were recorded from three different camera perspectives (2x Panasonic HC‐VX11 and 1x VeoCam1). Subsequently, two independent experts rated the performance on a scale of 1–10 (1 = insufficient and 10 = optimal).

FIGURE 1 Set‐up “Approach of the Ball to the Target” (left): field size = 12 × 8 m; subdivision into three fields of equal size; distance between cameras and field = 3 m and set‐up “using gaps” (right): field size = 8 × 7 m; width of the middle zone = 1 m; and distance between cameras and field = 3 m.

The rating was based on the quality of the pass and the difficulty of the situation. The quality of a pass was rated as high if the player played the ball in the direction of the target area, bridged as great a distance as possible, and the ball reached the teammate so that he could initiate a follow‐up action. In addition, the difficulty of the situation was evaluated. The number of ball contacts, the passing distance, and the number of opponents in the immediate vicinity of the passing player were taken into account. Through both components, an overall judgment of the individual performance was formed.

The definitions for the criteria quality and difficulty can be found in Supporting Information S1: A. The score of a pass was calculated in the ratio 2:1 (quality: difficulty). The total scores of all runs of a sequence were averaged. Then, the individual scores of each sequence of a player were averaged as the final score. Finally, the average of the two final scores of the independent raters was calculated, giving the overall score of the player.

In Test 2 “Using Gaps” (UG, Figure 1), the 8 × 7‐m field was divided into three fixed zones. There were two outer zones, each with two attackers, and a smaller middle zone in which three defenders operated. The attackers were allowed to pass the ball to each other in their field but were not allowed to move with the ball on their feet. The goal was to pass the ball flat to the other side as many times as possible without a defender getting to the ball. After every 2 min, the positions were systematically changed. Each player was allowed to attack twice (two sequences per player).

Each pass of an attacker was evaluated with the previously described dimensions “quality” and “difficulty” (for calculation see ABT). In addition, pass attempts and missed passes were counted. For the analysis, both the number of missed passes and the incomplete pass rate (IPR) were considered. The IPR relates the number of total passes in relation to incomplete passes. The calculation follows the calculation of the dimension's difficulty and quality.

2.3 Procedures

Data collection was performed in two steps. First, the seven cognitive tests (RT, DT, TMT A, NBN, and peripheral perception test) were collected using the computerized system (Schuhfried, 2016). Standardized instructions were used for each test situation. The total duration was approximately 50 min, and each measurement was performed in the same test order in a quiet room in the absence of other people. Prior to the computer‐based cognitive tests, the personal data of each player, such as age, height, weight, playing position, and years of soccer experience, were collected.

Then, the game test situation 1 ABT and the game test situation 2 UGs were recorded on a soccer field of the corresponding soccer club. Participants received standardized instructions (Memmert, 2004) and had the opportunity to familiarize themselves with the test situation.

All measurements took place over a period of 1 week. Each player completed both the game‐testing situations and the computerized cognition test within 24 h. Ethical approval for the present study was obtained from a local ethics committee (AZ 2017_106).

2.4 Statistical analysis

A Pearson product‐moment correlation analysis between the variables of the general perceptual‐cognitive abilities and the outcome variables of the sport‐specific on‐field tests was conducted. Alpha was set at 0.05 and effect sizes were calculated in terms of correlation coefficient (r) with values 0.10, 0.30, and 0.50 referring to small, moderate, and large correlations.

Subsequently, a four‐step hierarchical regression analysis was conducted to identify which variables determine the rating “UGs,” the rating “ABT,” the number of incomplete passes, and the IPR.

In the first step, the variables DT correct, DT wrong, and in the second step, the parameter NBN mistakes were included. In the third step, outcome variables of RT were entered (RT average motor time, RT reacted correctly, RT average motor time, and RT reacted incorrectly). In step four, variables of the TMT were added (TMT quotient B/A, TMT time for part A, and TMT time for part B).

However, there are two missing records for RT. For this reason, the sample size in the analysis of variance and correlation analysis for the RT value is N = 28. Therefore, the block regression was performed with the sample size of N = 28. All analyses were performed with the IBM SPSS Statistics for MacOS, Version 27.0. (IBM Corp., “IBM SPSS”).

3 RESULTS

The variables RT reacted incorrectly (r = −0.400 and p < 0.05) and NBN mistakes (−0.388 and p < 0.05) correlate negatively with the rating in the sport‐specific on‐field test ABT. Accordingly, participants with a high rating in ABT had less incorrect answers in RT and made less mistakes in NBN.

There was a significant negative correlation between TMT time for part A and rating in sport‐specific on‐field test UG (r = −0.383 and p < 0.05) (Supporting Information S1: B). Consequently, players with a high rating UG completed the TMT part A quickly.

There were significant correlations between the following factors and the number of incomplete passes: DT wrong (r = 0.478 and p < 0.01), DT correct (r = −0.403 and p < 0.05), TMT time for part A (r = 0.500 and p < 0.01), and TMT quotient B/A (r = −0.380 and p < 0.05). This means players that committed more incomplete passes did more mistakes and less correct answers in DT. Those players also took longer to complete TMT part A and had lower TMT quotients B/A.

Moreover, significant correlations between the IPR and the following variables were detected: DT wrong (r = 0.572 and p < 0.01), RT reacted incorrectly (r = 0.520 and p < 0.01), RT reacted correctly (r = −0.408 and p < 0.05), TMT time for part A (r = 0.533 and p < 0.01), and TMT quotient B/A (r = −0.380 and p < 0.05). Hence, athletes with high IPRs had more wrong answers in DT and RT and less correct answers in RT. In addition, these athletes took longer to complete TMT part A and had lower TMT quotients B/A.

The stepwise regressions analysis for rating ABT and rating UG was not significant for the overall models (Supporting Information S1: C and D).

Step one and step two of the regression analysis for the number of incomplete passes were significant for the overall model (Table 2) with a higher R 2 for step one (F = 7.044; p = 0.004; and R 2 = 0.309) than step two (F = 4.560; p = 0.012; and R 2 = 0.283). The predictor variable “wrong answers in the DT” thus explained 30.9% of the variance.

TABLE 2 Summary of the four‐step hierarchical regression analysis for incomplete passes.

Variables	Step 1	Step 2	Step 3	Step 4	
B	ß	B	ß	B	ß	B	ß	
1.	DT wrong (n)	0.051	0.389	0.052	0.391	0.066	0.498	0.062	0.474	
DT correct (n)	−0.046	−0.340	−0.050	−0.365	−0.030	−0.217	−0.018	−0.130	
2.	NBN mistakes (n)			−0.016	−0.058	−0.019	−0.069	0.023	0.085	
3.	RT average motor time (s)					6.978	0.064	−1.156	−0.011	
RT average response time (s)					11.312	0.191	14.291	0.241	
RT reacted correctly (n)					−0.628	−0.113	0.838	0.151	
RT reacted incorrectly (n)					−0.066	−0.037	−0.586	−0.332	
4.	TMT quotient B/A (n)							−3.259	−0.440	
TMT time for part A (s)							0.267	0.372	
TMT time for part B (s)							0.062	0.150	
R 2	0.309**		0.283*		0.190		0.335		
Note: Bold values show significant values.

Abbreviations: B, beta (non‐standardized coefficient); DT, determination test; n, number; NBN, n‐back test; R 2, R‐squared coefficient of determination; RT, reaction test; s, seconds; TMT, trail‐making test; ß, beta (standardized coefficient).

*p < 0.05, **p < 0.01.

All steps of the regression analysis for the IPRs were significant for the overall model (Table 3) with the highest R 2 for step one (F = 9.591; p = 0.001; and R 2 = 0.389) and step four (F = 2.894; p = 0.026; and R 2 = 0.412). Step one to four revealed a significant effect on DT wrong (p < 0.05). Participants with more wrong answers in DT had higher IPRs.

TABLE 3 Summary of the four‐step hierarchical regression analysis for incomplete pass rate.

Variables	Step 1	Step 2	Step 3	Step 4	
B	ß	B	ß	B	ß	B	ß	
1.	DT wrong	0.269*	0.499	0.267*	0.496	0.288*	0.534	0.265*	0.492	
DT correct	−0.160	−0.289	−0.135	−0.243	−0.003	−0.006	0.033	0.590	
2.	NBN mistakes			0.116	0.103	0.053	0.047	0.201	0.179	
3.	RT average motor time (s)					79.779	0.179	58.256	0.131	
RT average response time (s)					48.713	0.201	72.771	0.301	
RT reacted correctly					−3.721	−0.164	0.458	0.020	
RT reacted incorrectly					1.114	0.155	0.596	0.083	
4.	TMT quotient B/A							−17.182	−0.568	
TMT time for part A (s)							−0.201	−0.690	
TMT time for part B (s)							0.518	0.304	
R 2	0.389**		0.373**		0.350*		0.412*		
Note: Bold values show significant values.

Abbreviations: B, beta (non‐standardized coefficient); DT, determination test; n, number; NBN, n‐back test; R 2, R‐squared coefficient of determination; RT, reaction test; s, seconds; TMT, trail‐making test; ß, beta (standardized coefficient).

*p < 0.05, **p < 0.01.

4 DISCUSSION

The aim of the present study was to analyze the relation between general perceptual‐cognitive abilities and sport‐specific performance in youth competitive soccer. Specifically, we analyzed the interactions of general cognitive abilities of selective attention, cognitive flexibility, inhibition, working memory, peripheral perception, choice response (using the VTS), and soccer‐specific performance in game test situations (ABT, UGs, and Pass Performance) in youth competitive soccer players.

Regarding the sport‐specific on‐field test UGs, our results showed that the performance was correlated with the part A TMT A which was used to assess cognitive flexibility, a core EF. Players with a high rating in UG were faster to complete the TMT part A, suggesting that cognitive flexibility is a relevant general perceptual‐cognitive ability in a soccer game. Höner (2024) describes cognitive flexibility as being able to make decisions, adapt to new situations, and change perspectives. In the game situation, a planned action must be recognized and adapted motorically in the shortest possible time. This complements results of previous studies, which showed better cognitive flexibility and inhibitory control in elite and sub elite young soccer players than in amateurs (Huijgen et al., 2015; Verburgh et al., 2014). Moreover, Vestberg et al. (2012) were able to show that the capacity of the EFs, in particular the core EF, predicted successful performance in terms of goals and assists in the subsequent 2 years. In a later study, Vestberg et al. (2017) also found a significant relationship between goals scored and working memory as well as cognitive flexibility. Thus, with respect to these interdependencies, a game‐oriented training of EF, especially cognitive flexibility, might be relevant to improve the game performance and creativity (Vestberg et al., 2017, 2020).

With respect to the sport‐specific on‐field test ABT, our results revealed a relationship between responses to the RT and working memory performance (NBN). Participants with a high rating in ABT had less incorrect reactions in RT and made less mistakes in NBN. Both the cognition tests as well as the sport‐specific on‐field tests have to be performed under time pressure or under pressure from reacting on opponents. Participants with higher attentional resources might have been able to divide attention successfully and showed greater motor performance. Such considerations are also discussed by Song (2019).

For the success of a soccer game, the pass performance is crucial. Within our experiment, we were able to show that the pass performance was correlated with the test results of the RT, the DT, as well as the TMT A and B. More precisely, players that committed more incomplete passes made more mistakes and achieved less correct answers in RT and DT and showed fewer positive results on the TMT. All three correlations support the previous findings on the necessity of cognitive flexibility and inhibitory control for successful soccer performance. During soccer, players are faced with a rapidly changing complex environment. Therefore, the individuals' cognitive‐motor performance might determine their playing abilities (Williams, 2000).

The stepwise regression analysis revealed a significant effect of the DT when predicting the number of incomplete passes and the IPR in sport‐specific on‐field tests in youth athletes. Overall, the predictor variable “wrong answers” in the DT explained 30.9% of the variance in incomplete passes and 39% of the variance in IPR. The regression analysis revealed that the wrong reactions during the DT have the greatest influence on the successful passing rate. This has not been reported in any other study so far. However, the results of our study imply that general perceptual‐cognitive abilities, such as selective attention and reaction time, assessed using the DT might help to predict performance in soccer‐specific test situations. Due to the time‐efficient feasibility of computer‐based testing procedures, this could be of particular relevance for talent identification and development for sports practice.

4.1 Limitations and future research

Next to the strengths of this study, it should be considered that the participants of this study were 13 and 14 years old. In this age range, the psychological as well as the physical stage of development varies. For some, puberty may have just begun; for others, it may be further along. This also implies that there might be differences in cognitive abilities, especially for EFs (Fung et al., 2022). Therefore, in future analyses, the biological age should also be assessed. Moreover, in future studies, it needs to be evaluated whether the results of the present study can be reproduced by youth athletes in higher age groups, such as U15–U17 or in female athletes.

To date, expertise research in sport science has rarely succeeded in investigating domain‐specific cognitive abilities in the context of domain‐general cognitive abilities, thus linking the cognitive‐component‐ability approach with the expert‐performance approach (Kalén et al., 2021). The present study aimed to contribute to filling this theoretical gap by conceptually integrating the expert performance approach and the cognitive component skill approach and examining sport‐specific and cross‐domain cognitive processes together. The present study is thus one of the few which analyzes the relationships between general perceptual‐cognitive abilities and sport‐specific performance in competitive youth soccer.

For this study, we used game test situation paradigms developed in a previous study (Memmert, 2004). In doing so, essential aspects of the sport‐specific complexity, such as, among others, the density of space and the counter‐pressure acting with it, are not considered in a way that is true to the game. In the sense of a multi‐professional approach, in future studies, experts from science and practice should work together to design test situations that are as close to the game as possible, so that the ecological validity is taken into account more strongly, while considering the necessary objectivity of the measurement methods. Potential insights into relevant brain‐body dynamics might be gained with the mobile brain imaging approach (MoBI) using electroencephalography or functional near‐infrared spectroscopy in real world scenarios (e.g., Perrey & Besson, 2018; Studnicki & Ferris, 2024).

5 CONCLUSION

This study provides initial insights into the relationship between general cognitive abilities and sport‐specific performance in an ecological approach combining cognitive tests with real‐world performance tests. In particular, core EFs, such as cognitive flexibility and selective attention, appear to correlate to performance in the sport‐specific tests performed. Thus, this study brings together key approaches in expertise research and makes a significant contribution to a better understanding of expertise in soccer. Future studies should approach an ecologically valid sport‐specific test situation, taking into account the complexity of the soccer‐specific requirement and the necessary objectivity of the measurement procedure, with the use of MoBI approaches to better understand brain–body dynamics.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflicts of interest.

Supporting information

Supporting Information S1

ACKNOWLEDGMENTS

The authors would like to express their gratitude to all of the athletes and coaches who participated in this study. The authors also acknowledge colleagues and students who acted as members of the research team for all their efforts in data capture.
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