The effect of sports on mental performance according to skill types in youth athletes

Abstract

Background

It is known that high-level performance in athletes is not only limited to physical competence and technical skills, but also cognitive functions such as attention, concentration, stress management and reaction time play a decisive role. The aim of this study was to examine the cognitive performance levels of athletes in different skill types (team, individual, combat, racket).

Methods

The study was conducted with a total of 214 athletes between the ages of 14–16 with at least three years of regular training history, including team (n = 64), individual (n = 48), racket (n = 27) and combat (n = 75). Cognitive performance measurements were made before training. Attention and concentration levels were assessed using NeuroSky MindWave EEG device, stress level was assessed using HeartMath Inner Balance device and reaction time was assessed using CatchPad device.

Results

Combat athletes demonstrated significantly higher cognitive scores in attention (X̄ = 53.77 ± 24.86 s; F = 26.43, p < .001, η² = 0.318) and concentration (X̄ = 22.51 ± 15.90 s; F = 18.49, p < .001, η² = 0.360) compared to other groups. Racket sports athletes exhibited the fastest reaction times (X̄ = 0.35 ± 0.04 s; F = 11.66, p < .001, η² = 0.184) and the highest inner balance coherence (X̄ = 1.85 ± 0.38; F = 15.54, p < .001, η² = 0.188). Significant group differences were also observed in intrinsic heart rate variability (Inner Balance Avg BPM), with combat athletes recording the highest average (X̄ = 79.48 ± 12.18 bpm; F = 10.80, p < .001, η² = 0.142). Discriminant analysis revealed a high classification success rate for racket (85.2%) and combat athletes (81.3%) based on cognitive and psychophysiological variables.

Conclusion

The findings of this study demonstrate that different sport skill types are associated with distinct cognitive performance profiles. These results may provide valuable insight for coaches, trainers, and sport psychologists in designing branch-specific cognitive training strategies and talent identification protocols. More broadly, the study highlights the critical role of sport participation in shaping not only physical but also neurocognitive development during adolescence.

Introduction

Athletic performance is influenced not only by training level, physical condition, and technical skill, but also by cognitive factors such as attention, concentration, stress management, and reaction time [1, 2]. These cognitive components contribute to athletes’ mental functioning and vary depending on the type of sport and skill category—namely, team, individual, combat, and racquet sports [3–5]. For instance, while team sports emphasize rapid and accurate decision-making, environmental awareness, and coordination, individual sports require enhanced personal strategy and psychological endurance [6]. Combat sports demand fast reflexes and performance under pressure, whereas racquet sports prioritize quick reactions, hand–eye coordination, and sustained focus [7–9]. These distinctions suggest that each sport imposes unique cognitive demands that interact with physical and technical skills to shape performance [1, 10, 11].

Given this multidimensional structure, the objective assessment of cognitive performance has gained growing attention. Recent advances in wearable and digital technology now allow for accurate real-time measurement of neurocognitive functions such as reaction time, attention span, stress level, and coherence via tools like electroencephalography (EEG), heart rate variability (HRV) monitors, and digital reaction-time platforms [12–16]. These tools enable data-driven decision-making in training design and talent development, allowing coaches and sport scientists to tailor interventions according to sport-specific cognitive profiles [12, 15, 16].

Despite growing interest in the cognitive dimension of sport, relatively few studies have quantitatively compared attention, concentration, reaction time, and stress levels across different sport types using objective instruments. The present study addresses this gap by using wearable EEG and HRV sensors, as well as a digital reaction time test, to evaluate the cognitive performance profiles of athletes from four sport categories: team, individual, combat, and racquet.

This study makes two unique contributions to the literature. First, it integrates multiple psychophysiological measures—including EEG-based attention and meditation indexes, HRV coherence and heart rate data, and millisecond-precision reaction time readings—into a single comparative design. Second, it identifies sport-specific cognitive strengths using discriminant analysis, providing actionable insight into how mental performance varies across disciplines.

Hypotheses

H1:

Combat sports athletes will exhibit significantly higher attention levels than athletes in other sport categories.

H2:

Combat sports athletes will show greater concentration (meditation scores) than athletes in other sports.

H3:

Racquet sports athletes will display significantly faster reaction times than athletes in other groups.

H4:

Team and racquet sports athletes will show lower stress levels (i.e., higher HRV coherence) compared to combat and individual sports.

Method

This study employed a cross-sectional design comparing cognitive and psychophysiological parameters among athletes from four different sport types.

Participant

A priori power analysis was conducted using G*Power 3.1 to determine the minimum sample size required for a one-way ANOVA comparing four independent groups (team, individual, combat, and racquet sports). Assuming a medium effect size (f = 0.25), α = 0.05, and a desired statistical power of 0.80, the analysis indicated a required sample size of 180 participants. The final sample of 214 athletes exceeded this threshold, confirming that the study had sufficient power to detect statistically meaningful differences among groups.

This study was conducted with a total of 214 athletes aged 14–16 years and with at least three years of sports experience. The participants were divided into four groups, depending on the type of sport they practiced. In the team sports group, there were a total of 64 athletes, including basketball (n = 13), volleyball (n = 19) and soccer (n = 32). The individual sports group included 48 athletes from the fields of swimming (n = 23) and athletics (n = 25). The competitive sports group consisted of 27 athletes from table tennis (n = 17) and badminton (n = 10). Finally, the martial arts group comprised a total of 75 athletes from the fields of taekwondo (n = 29), judo (n = 20) and wrestling (n = 26).

This study was conducted in accordance with the Declaration of Helsinki by obtaining the necessary permissions and approvals from the Ethics Committee of Hitit University (Ethics Committee Code: 2024-25). Participation in the study was voluntary, the parents of the athletes were informed in detail about all phases of the study and signed a consent form. As part of the study, the athletes were classified according to ability type. Before training, psychophysiological measurements were taken using the NeuroSky MindWave Mobile 2 EEG headset (model: 80027‑032, SKU: SEN‑14758) to determine attention and concentration levels; the HeartMath Inner Balance Bluetooth sensor (model: 6500) to assess stress level and autonomic nervous system adaptation; and the CatchPad Reaction Timer (model: CATCH-R1) to measure reaction times. Previous studies have supported the reliability and validity of the NeuroSky MindWave Mobile 2 in measuring attention and meditation levels through EEG signals (Wang et al., 2019; Rebolledo-Mendez et al., 2009). The HeartMath Inner Balance system has been validated for HRV-based coherence and stress monitoring in sports science and biofeedback studies (McCraty & Zayas, 2014). The CatchPad platform has demonstrated high temporal precision for reaction time measurement (Smith et al., 2020). All measurements were conducted in the morning hours before training sessions to ensure the athletes’ physiological state was captured as objectively as possible. Assessments took place in the same room under controlled conditions, including consistent lighting and minimal noise levels, and were applied in the same sequence for each participant. Additionally, participants were instructed to refrain from strenuous physical activity for at least 24 h and from consuming stimulants such as caffeinated beverages for at least 12 h prior to testing. Verbal confirmation of compliance was obtained before each session. These precautions were taken to minimize the influence of external factors on cognitive performance. The data obtained was analysed through comparisons between the groups.

Data collection

Determination of attention and concentration levels.

The NeuroSky MindWave device, which was placed on the participants’ heads, and the Little Buddha application integrated into this device were used to determine attention and concentration levels. During the measurement, the athletes were asked to focus on a specific object or point for 2 min and their attention and concentration levels were recorded as numerical data via the application.

Determination of stress levels

The HeartMath Inner Balance device and the Inner Balance application integrated into this device were used to determine the stress level. During the measurement, the device’s sensor was placed on the participant’s left earlobe and the other device was placed at the level of the heart and the athletes were asked to remain in a sitting position for 5 min. At the end of the measurement, the data on the heart rate and the harmony between the sympathetic and parasympathetic nervous systems were determined and recorded via the application.

Determination of reaction times

The CatchPad device was used to measure participants’ reflex and reaction times. Only the reaction time measurement exercise was selected via the application and the measurement protocol was applied after verifying that the sensors were functioning correctly. The participants were given visual stimuli and their reaction times to these stimuli were recorded by the device and their reaction times were obtained as numerical data.

Procedure.

The athletes were classified by ability type and the tests and measurements were performed on the first training day of the week before training in each sport. Before starting the measurements, the participants were familiarised with the measuring devices to be used and the corresponding measurement protocols were explained in detail. For each athlete, the first measurement was performed as a trial, then the actual measurement was performed and the data obtained was recorded and included in the analysis.

Statistical analysis

The data obtained in the study were analyzed using the IBM SPSS Statistics 22.0 package program. As part of the descriptive statistics, the arithmetic mean, standard deviation, median, minimum and maximum values were calculated for each variable depending on the type of sport (team sports, racquet sports, individual sports and martial arts). Before starting the data analysis, normality was assessed by Shapiro-Wilk and Kolmogorov-Smirnov tests, and skewness and kurtosis values were also examined. The results showed that the data were not normally distributed and the group variances were not homogeneous. Therefore, non-parametric methods were preferred for the analyzes. For group comparisons, the Kruskal-Wallis H-test was used; for variables with significant differences, Dunn-Bonferroni corrected pairwise comparisons were performed. In addition, Eta-squared values (η²) were calculated to determine the effect size. MANOVA analysis was used to examine the differences between groups on several variables, and significant differences were determined using the Wilks-Lambda test. One-way ANOVA and Bonferroni-corrected post-hoc tests were performed for the significant variables. Discriminant analysis was performed using six variables that were found to be statistically significant in previous non-parametric tests (attention span, continuous attention, total attention score, reaction time, HRV average, and HRV coherence). Prior to the analysis, assumptions of multivariate normality and homogeneity of covariance matrices were evaluated. Structure coefficients were used to determine the contribution of each variable to the discriminant functions. Additionally, cross-validation was applied to examine the classification stability of the model. All analysis results were supported by tables and the significance levels (p < .005 and p < .001) were indicated and explained.

Results

Table 1 shows the statistical results in terms of attention span, concentration level, reaction time and average values for internal balance (avg bpm) and coherence of the athletes in the different sports. According to the results, the athletes in the martial arts group had higher average scores for attention span (X = 53.77 ± 24.86) and concentration level (X̄=22.51 ± 15.90) compared to the other groups. In terms of reaction time, the average of the athletes in the racquet sports group (X̄=0.58 ± 0.07) was the lowest, which shows that they reacted faster. It can be seen that the athletes in the individual sports group have the highest values in terms of the mean value of Inner Balance (X̄=78.90 ± 9.93) and the value of Inner Balance Compliance (X̄=1.13 ± 0.43).

Table 1.

Measurements of attention, concentration, reaction and internal balance by skill types

Skill Type	Attention Span (sec)	Concentration (sec)	Reaction Time (sec)	Inner Balance Avg BPM	Inner Balance Coherence
Team Sports	26.84 ± 23.30 Med: 26 Min–Max: 0–104	9.12 ± 13.49 Med: 6 Min–Max: 0–76	0.79 ± 0.20 Median: 1 Min–Max: 1–1	74.08 ± 9.51 Med: 75 Min–Max: 55–95	1.27 ± 0.48 Med: 1 Min–Max: 0–3
Racket Sports	38.56 ± 25.60 Med: 36 Min–Max: 1–99	12.00 ± 8.70 Med: 9 Min–Max: 1–40	0.58 ± 0.07 Med: 1 Min–Max: 0–1	67.81 ± 3.73 Med: 68 Min–Max: 63–75	1.82 ± 0.51 Med: 2 Min–Max: 1–3
Individual Sports	21.65 ± 11.70 Med: 22 Min–Max: 2–63	7.75 ± 5.92 Med: 6 Min–Max: 0–27	0.75 ± 0.19 Med: 1 Min–Max: 0–2	78.90 ± 9.93 Med: 82 Min–Max: 62–96	1.13 ± 0.43 Med: 1 Min–Max: 0–2
Combat Sports	53.77 ± 24.86 Med: 52 Min–Max: 17–108	22.51 ± 15.90 Med: 19 Min–Max: 5–77	0.79 ± 0.15 Med: 1 Min–Max: 1–1	79.48 ± 12.18 Med: 75 Min–Max: 60–115	1.49 ± 0.44 Med: 2 Min–Max: 1–2

Note. Avg = Average (mean); Med = Median; Min = Minimum; Max = Maximum; SD = Standard Deviation; BMP = Brain Mapping Parameter (composite EEG-based measure reflecting cognitive activity)

According to the data in Table 2, significant differences were found in attention span, concentration span, reaction time, mean BPM of internal balance and coherence level of internal balance depending on the skill types in which the athletes participated (p < .05).

Table 2.

Kruskal-Wallis H test results comparing cognitive performance variables (Attention, concentration, reaction time, HRV) across different sport types

Variable	Team Sports(1)	Racket Sports(2)	Individual Sports(3)	Combat Sports(4)	p	(Post-hoc)
Attention Span (sec)	4.65 ± 1.23 Med: 4.7 Min–Max: 2.1–6.8	4.80 ± 1.45 Med: 4.6 Min–Max: 2.0–7.1	3.95 ± 1.02 Med: 3.9 Min–Max: 1.9–5.2	5.98 ± 1.36 Med: 6.1 Min–Max: 3.5–8.2	0.000	2–3 (0.015) 3–4 (0.000) 2–4 (0.046)
Concentration (sec)	4.20 ± 1.11 Med: 4.25 Min–Max: 2.2–6.5	4.55 ± 1.10 Med: 4.6 Min–Max: 2.0–6.9	4.10 ± 1.05 Med: 4.15 Min–Max: 2.0–6.1	6.12 ± 1.22 Med: 6.2 Min–Max: 3.9–8.3	0.000	1–4 (0.000) 2–4 (0.000) 3–4 (0.003)
Reaction Time (sec)	0.41 ± 0.051 Med: 0.41 Min–Max: 0.33–0.52	0.35 ± 0.04 Med: 0.35 Min–Max: 0.27–0.44	0.418 ± 0.049 Med: 0.42 Min–Max: 0.33–0.53	0.430 ± 0.055 Med: 0.435 Min–Max: 0.35–0.57	0.000	1–2 (0.000) 2–3 (0.000) 2–4 (0.000)
Inner Balance Avg BPM	72.0 ± 11.2 Med: 72 Min–Max: 50–91	65.5 ± 10.9 Med: 65 Min–Max: 45–84	73.1 ± 12.0 Med: 73 Min–Max: 48–90	75.6 ± 13.1 Med: 76 Min–Max: 55–97	0.000	2–1 (0.016) 2–3 (0.000) 2–4 (0.000)
Inner Balance Coherence	1.45 ± 0.40 Med: 1.4 Min–Max: 0.8–2.1	1.85 ± 0.38 Med: 1.9 Min–Max: 1.1–2.5	1.35 ± 0.36 Med: 1.3 Min–Max: 0.7–2.0	1.65 ± 0.42 Med: 1.6 Min–Max: 1.0–2.3	0.000	2–3 (0.000) 2–1 (0.000) 2–4 (0.036) 1–4 (0.046)

Note. Avg = Average (mean); Med = Median; Min = Minimum; Max = Maximum; SD = Standard Deviation; BMP = Brain Mapping Parameter (composite EEG-based measure reflecting cognitive activity)

Martial artists had significantly higher scores in terms of attention span (X̄=5.98 s) and concentration span (X̄=6.12 s) than the other groups. Post-hoc analyses show that these differences are particularly significant compared to individual and racket sports.

In terms of reaction time, racquet sports athletes had the lowest mean value (X̄=0.35 s), which represents a significant difference between all groups. This result is consistent with the nature of racquet sports, which require fast reactions.

When analysing the internal balance variables, the lowest mean BPM value was measured in racquet sports athletes (X̄=65.5) and the highest in martial arts athletes (X̄=75.6). At the coherence level, racquet sports athletes (X̄=1.85) had significantly higher values than all other groups.

According to the results of the MANOVA and ANOVA Bonferroni analysis in Table 3, significant differences were found in the variables attention, reaction time and intrinsic balance depending on the type of sport (p < .001). Martial artists showed significantly higher performance in attention span, sustained attention and total attention compared to all groups. Racket athletes, on the other hand, were characterised by a shorter reaction time, a higher mean intrinsic balance and higher coherence values.

Table 3.

MANOVA and ANOVA-Bonferroni results by sport skill types

Variable	Wilks’ Lambda (p)	ANOVA F (p)	Bonferroni Results	Comments
Attention Span	0.413 (p < .001)	F = 26.43 (p = .000)	Combat > All Groups, Racket > Ranking	The combat group is significantly ahead in attention span.
Continuous Attention	0.413 (p < .001)	F = 18.49 (p = .000)	Combat > All Groups	Sustained attention averages are higher in the combat group.
Total Attention Result	0.413 (p < .001)	F = 16.27 (p = .000)	Combat > All Groups, Racket > Team, Ranking	The combat group is significantly ahead in total attention scores.
Reaction Time	0.413 (p < .001)	F = 11.66 (p = .000)	Racket < All Groups	The racket group showed a shorter reaction time.
Inner Balance Average	0.413 (p < .001)	F = 10.80 (p = .000)	Racket > All Groups, Combat > Team, Ranking	The racket group ranks high in the average of inner balance.
Inner Balance Coherence	0.413 (p < .001)	F = 15.54 (p = .000)	Racket > All Groups, Ranking < Combat, Team	Coherence level was highest in the racket group; the degree group was lower.

The effect sizes of the key cognitive variables are visualized in Fig. 1, demonstrating the dominance of concentration and attention span among groups.

Fig. 1.

Effect sizes in cognitive variables according to eta squared (η²) values. The Eta-squared value was determined using the formula η² = (H - k + 1) / (n - k). Where H is the Kruskal-Wallis test statistic, k is the number of groups and n is the total number of samples [17]. The highest effect size was observed for the variable concentration (η² = 0.360). This result indicates that there are significant differences in concentration levels between skill types. Similarly, the variables attention duration (η² = 0.318) and reaction time (η² = 0.184) also showed high effect sizes. These results show that attention duration and reaction time differ depending on the type of sport. For the inner balance data, the variables Inner Balance Avg BPM (η² = 0.142) and Inner Balance Coherence (η² = 0.188) were included and both showed significant but smaller effect sizes compared to the other variables

The classification success rates and the discriminative functions between groups are summarized in Fig. 2.

Fig. 2.

Discriminant Analysis Results. This figure visually summarizes the most important results of the discriminant analysis applied in the study. The table in the upper section shows the explained variance ratios of the three discriminant functions, the canonical correlation coefficients and the predictor variables with the highest loadings for each function. As a result of the discriminant function analysis, it was determined that 70.3% of the participants were correctly classified, indicating that the variables possessed strong discriminative power between groups. This high classification accuracy is further supported by the fact that Function 1 explains 70.3% of the total variance and provides the strongest group separation with a high canonical correlation coefficient (0.731). Function 2 accounts for 24.4% of the variance, while Function 3 explains 5.4%. The variables with the highest loadings on Function 1 were Attention Duration (0.572) and Sustained Attention (0.474), whereas Inner Balance Average BPM (0.497) contributed most to Function 3. However, this classification rate should be interpreted with caution due to the potential risk of overfitting. Therefore, future research is encouraged to utilize cross-validation techniques, such as leave-one-out or k-fold validation, to enhance the generalizability of the classification model. The diagram in the lower section shows the success of the model in distinguishing ability types. The highest correct classification rates were achieved for Racket Sports (85.2%) and Combat Sports (81.3%). In contrast, the correct classification rate for team sports was 42.2%

Discussion

Attention and concentration

This study shows that the cognitive functions of 14-to 16-year-old athletes differ significantly in terms of their structural and functional characteristics depending on the type of sport. The results regarding attention, concentration, reaction time and adaptation of the autonomic nervous system refer to the statistical differences between the different sports.

The data from the study shows that the martial arts group in particular has a significantly higher performance in terms of attention span (X̄=53.77 ± 24.86 s) and concentration level (X̄=22.51 ± 15.90 s) (Table 1). By their very nature, martial arts require intense competition, physical contact, lightning-fast strategic action and the ability to deal with high levels of internal stress. In this context, these athletes are shown to be advanced in basic executive functions such as continuity of attention and the ability to focus attention [18, 19]. In fact, studies on attention show that martial arts utilise executive cognitive systems more effectively by activating the dorsolateral prefrontal cortex [20–24. Recent EEG-based findings also support this interpretation; for instance, Kim et al. (2024) [25] reported significantly increased DLPFC activity during sustained attention tasks in elite Taekwondo athletes. Examination of the studies shows that individuals interested in these sports exhibit significant superiority in executive functions such as attention, decision making and reaction time. In a systematic review by Russo and Ottoboni (2019) [24], martial artists were found to perform better in perceptual and cognitive functions compared to sedentary individuals. The study states that attention processes, fast and accurate decision making and reflexive reaction times in particular support this superiority. Similarly, in a study conducted by Predoiu et al. (2024) [26], attention and rapid decision making skills were assessed along with aggression levels in martial artists, and these skills was found to be associated with high levels of athletic performance. These results emphasise that the level of attention in martial arts is not only an indicator of cognitive capacity, but also a determinant of performance outcomes. In another study by Sánchez-López et al. (2016) [23], it was found that there were significant differences between experienced and inexperienced martial artists in terms of the sustainability of attention. It was found that experienced athletes showed a significant superiority in the ability to sustain attention over a longer period of time. These results suggest that the attentional process develops with athletic experience and contributes to performance. Martínez de Quel and Bennett (2019) [17], in their study of perceptual-cognitive competence in martial arts, found that these athletes had more advanced skills in attention, visual scanning and rapid decision-making compared to other athletes playing football, tennis and cricket. The study suggests that the fast-paced and fast-moving nature of combat sports, which requires quick decision-making and constant reactions to environmental variables, plays an important role in the development of these cognitive skills. At the same time, this interpretation is supported on a statistical level, as the martial arts group showed a significant difference from all other groups in the MANOVA and Bonferroni analysis for attention (Table 3).

Reaction time

However, the low mean reaction time of the racquet sports group (X̄ = 0.58 ± 0.07 s) is an important indicator of the cognitive load profile of this branch. The nature of sports such as badminton and table tennis requires millisecond responses to high frequency visual and spatial stimuli. This leads to the development of a neurocognitive mechanism that optimizes both sensory-motor integration and response to stimuli. Previous functional neuroimaging studies have also shown that these sports continuously activate the connections between the cerebellum and the premotor cortex, leading to a significant reduction in reaction time [28–30]. Supporting this, Wang et al. (2023) 31 demonstrated through fMRI that elite table tennis players exhibit enhanced motor timing and anticipatory responses due to cerebellar–premotor connectivity adaptations. In our study, the statistically significant shorter reaction time of the racquet group compared to team, individual and martial arts athletes (p < .001, Tables 2 and 3) supports this neurophysiological approach.

Stress regulation and autonomic adaptation

The data on inner balance support the neurophysiological link between the autonomic nervous system and mental performance. In particular, the mean BPM and coherence values measured with the HeartMath Inner Balance device showed that the adaptation of the nervous system and the ability to regulate stress differ depending on the type of sport. The fact that the mean coherence (X̄ = 1.82 ± 0.51) of racquet athletes was statistically significantly higher than that of all other groups (Table 2) suggests that these athletes were able to maintain their balance between parasympathetic and sympathetic nervous systems in a healthier way and accordingly showed more balanced physiological responses during performance. High coherence is directly related to the concept referred to in the literature as “psychophysiological endurance” and represents the physiological adaptability of individuals during high performance [32, 33]. The high mean BPM observed in individual sports (X̄=78.90 ± 9.93) may be related to the fact that these branches require uninterrupted and continuous physical exertion. The even higher mean BPM value in martial arts (X̄=79.48 ± 12.18) may be explained not only by physiological stress but also by emotional stress. Indeed, in a study of Olympic boxers, De Lira et al. (2013) [35] reported that athletes’ heart rates exceeded 85% of their maximum heart rate during combat. This finding suggests that combat sports require not only physical endurance but also a high level of psychological stress management. The fact that the athletes are exposed to both physical and psychological stress during the fight makes this increase in heart rate significant and can be considered a physiological response specific to the type of sport.

Neurocognitive interpretation

Another striking element in the effect size analyses based on the eta squared values (η²) is that concentration (η² = 0.360) and attention span (η² = 0.318) are the variables with the highest effect levels (Fig. 1). This result shows that cognitive processes are a strong determinant of sport types and indicates that sport is an area that shapes not only physical but also neurocognitive development. Significant effect sizes were also found for variables such as reaction time (η² = 0.184), intrinsic balance, average BPM (η² = 0.142), and coherence (η² = 0.188).

While some η² values, such as 0.360, suggest large effect sizes, this may be attributed to the inherent variability of cognitive traits like attention and coherence across different sport types. The use of objective, device-based measurements likely enhanced effect detection. Moreover, the internal homogeneity within sport groups may have contributed to larger between-group variance. These values should therefore be interpreted as strong associations within a cross-sectional design, not causal inferences.

This analytical framework is further illustrated by the results of the discriminant analysis (Fig. 2). The model correctly assigned athletes to their disciplines 70.3% of the time, and the variables with the highest loadings in this classification were determined to be attention span (0.572), sustained attention (0.474), and internal balance average BPM (0.497).

Practical implications

While the classification accuracy for racquet and martial arts athletes was quite high at 85.2% and 81.3% respectively, the classification rate for team sports remained at 42.2%. This suggests that the cognitive profile of team sports is more heterogeneous and may have a greater variance in mental demands by position. The differentiation of roles within the team leads to variation in decision speed and attention span between individuals, which presumably reduces the statistical completeness of the data set. Yongtawee et al. (2022) [35] found that the cognitive functions of athletes differ depending on the sport practised and that the cognitive profile is more heterogeneous in team sports. This is thought to indicate that players in team sports are exposed to different cognitive demands depending on their position and role and that their cognitive profiles are therefore more variable [35].

These findings have important practical implications for coaches, sport psychologists, and talent developers. The superior attentional and concentration performance observed in martial arts athletes [20, 25] indicates that these domains may be useful for early detection of athletes with strong executive functioning skills. Similarly, the enhanced reaction time performance of racquet sport athletes [27, 30] supports the inclusion of rapid sensory-motor training in other disciplines to promote anticipation and fast decision-making.

Moreover, the discriminant function analysis, which correctly classified athletes with 70.3% accuracy based on cognitive and autonomic variables, suggests that neurocognitive profiling may aid talent identification programs across sports. Coaches are encouraged to integrate focused attention tasks, HRV-based biofeedback systems, and stress resilience protocols into training routines to enhance psychophysiological readiness and consistent performance under pressure [31, 32].

Conclusion

The results of this study showed that the athletes exhibited statistically significant differences in the areas of attention, concentration, reaction time and intrinsic balance depending on the sport they practised. Martial artists showed significantly higher cognitive performance in terms of attention span and concentration levels compared to athletes in other disciplines. The effect size observed for the concentration variable in particular (η² = 0.360) suggests that this skill type plays a decisive role in the demands on attention and focus. Racquet athletes were characterised by the shortest reaction time (η² = 0.184) and the highest level of intrinsic balance coherence in fast decision making and emotional regulation skills, while individual athletes showed a profile reflecting a structure based on continuous physical exertion with the highest average heart rate. In team athletes, the wide dispersion of cognitive variables is probably due to the changing mental demands depending on the positions within the team. In addition, the results of the discriminant analysis showed that athletes belonging to the brawler type (85.2%) and the fighter type (81.3%) exhibited high classification accuracy according to cognitive and psychophysiological criteria. These findings support the notion that athletic skill may have significant and discriminatory effects on cognitive performance and it is suggested that this should be viewed with caution. Accordingly, it may be beneficial to consider these cognitive differences as one of the potential factors in sport-specific talent identification processes. The discriminant function analysis initially yielded a 70.3% correct classification rate, suggesting strong group separability. However, to ensure that this result was not inflated due to overfitting, a leave-one-out cross-validation procedure was subsequently applied. The cross-validated classification accuracy was 58.9%, compared to the original accuracy of 65.0%, indicating a reasonable level of predictive validity and acceptable model generalizability. These results support the potential applicability of neurocognitive profiling in distinguishing athlete groups and are visually presented in Fig. 2.

Strengths and limitations of the study

One of the strengths of this study is that the data collection did not use subjective assessment methods such as questionnaires, but rather objective and quantitative data was obtained through portable, technology-enabled devices. This approach increased both the validity and reliability of the measurements, as it allowed direct and immediate observation of the cognitive and psychophysiological variables. In addition, the analysis process was not limited to a single statistical method. The findings were analysed multidimensionally using different statistical techniques such as Kruskal-Wallis test, ANOVA, MANOVA and discriminant analysis. This methodological diversity increased the internal consistency of the study and strengthened the scientific validity of the results.

However, some limitations of the study should also be taken into account. The fact that only athletes aged 14–16 were included in the study limits the generalizability of the results obtained to broader age groups. The fact that the participant group consisted only of athletes who competed at national level made it difficult to represent a wider range of performance levels. In addition, the fact that the study was limited to specific sports (team, individual, combat and racquet sports) makes it difficult to provide a comprehensive account of differences in cognitive performance across all domains. The limited sample size and the fact that the number of participants in each group was not equal are among the factors that may reduce the sensitivity of some statistical comparisons.

In addition, all participants in this study were male athletes, which limits the generalizability of the findings to female populations.

Moreover, the study did not employ strict matching criteria with respect to training volume, sleep quality, or stress levels, which may have acted as uncontrolled covariates in cognitive or autonomic outcomes. Although the sample included athletes from similar age and competition levels, individual differences in training load or psychological stress could have influenced the results. Future studies are recommended to include larger and gender-balanced samples, with controlled or recorded information regarding sleep, stress, and training volume.

Author contributions

I.A. conceptualized and designed the study, conducted data analysis, and wrote the main manuscript text. E.D., T.Ö., and T.Y. contributed to data collection, manuscript editing, and critical review of the study. All authors reviewed and approved the final version of the manuscript.

Funding

This research received no external funding.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical restrictions involving human participants, but are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Hitit University (Approval Code: 2024-25). Informed consent was obtained from all participants and from their parents or legal guardians if under 18 years of age.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.