# Influence of ball reception on multiple player tracking in a virtual soccer environment

Alexandre Vu, Anthony Sorel, Benoit Bideau, Richard Kulpa

► To cite this version:

Alexandre Vu, Anthony Sorel, Benoit Bideau, Richard Kulpa. Influence of ball reception on multiple player tracking in a virtual soccer environment. Virtual Reality, 2026, 30 (2), pp.67. 10.1007/s10055-025-01310-1. hal-05530191

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# Influence of ball reception on multiple player tracking in a virtual soccer environment

Alexandre Vu<fnref n="1" />☐, Anthony Sorel<fnref n="1" />, Benoit Bideau<fnref n="1" />, Richard Kulpa<fnref n="1" />

Received: 5 May 2025 / Accepted: 30 December 2025

© The Author(s) 2025

## Abstract

Prospective decision-making in soccer requires taking information about teammates and opponents while ensuring effective reception and control of the ball. This multitasking aspect of soccer has rarely been considered in laboratory studies examining the relationship between expertise and visual attention measured in a visual tracking task. This research explored the ability of soccer players to pay attention to their environment when receiving a ball in simulated backward passing situations in a CAVE. Twenty-four soccer players, 12 high-skilled and 12 less-skilled, participated in this study. The visual tracking performance and global displacement of both groups were compared across different experimental conditions, which involved monitoring virtual teammates and opponents, receiving a virtual ball, or performing both tasks simultaneously. When the virtual ball was present in the scene, visual tracking performance decreased, and the global displacement increased. However, no additional differences were observed when ball reception was required. Furthermore, no differences between the two groups of participants were observed under any experimental condition. Adding the possibility to interact with the ball appears to engage participants in movement, introducing a constraint that exists in real game situations when perceiving teammates and opponents. However, the relationship between the perceptual-cognitive skills involved in visual tracking and expertise in soccer remains unclear. Further adaptations to the experimental protocol and the virtual environment are required to thoroughly investigate soccer players’ ability to interact with their complex and dynamic performance environment.

**Keywords** Soccer · Visual attention · Virtual reality · CAVE

## 1 Introduction

Soccer players must be aware of the positions of their teammates and opponents to make appropriate decisions while running and/or interacting with the ball. All these activities require visual attention. How attention is allocated may induce inappropriate tactical choices (Memmert and Furley 2007), degrade motor performance (Fleddermann

and Zentgraf 2018) and affect postural stability (Ren et al. 2022). Directly assessing perceptual-cognitive abilities involved in real games is challenging. Standardized and reproducible off-field exercises are therefore necessary to objectively measure differences between players or monitor their development over a season or throughout their career.

Off-field evaluations based on Multiple-Object-Tracking paradigm (MOT) (Pylyshyn and Storm 1988) are traditionally employed to assess the ability of athletes to dynamically and flexibly allocate attention in space (Memmert et al. 2009; Faubert 2013; Jin et al. 2020; Liu et al. 2024). MOT can be described as a visual tracking task in which several moving targets have to be monitored for several seconds in a bounded space shared by distracting objects of similar appearance. Appropriate attention allocation between targets over time is required to avoid any loss or confusion with a distractor (Drew et al. 2013). Visual tracking performance in this context is measured as the ratio of successfully tracked targets to the total number of displayed targets

<footnote>✉ Alexandre Vu
alexandre.phi.vu@gmail.com</footnote>

<footnote>Anthony Sorel
anthony.sorel@univ-rennes2.fr</footnote>

<footnote>Benoit Bideau
benoit.bideau@univ-rennes2.fr</footnote>

<footnote>Richard Kulpa
richard.kulpa@univ-rennes2.fr</footnote>

<footnote n="1">¹ Univ. Rennes 2, Inria, M2S, 35000 Rennes, France</footnote>
(Hulleman 2005). Visual tracking tasks are used to study expertise because the induced attentional demands, and thus the involved perceptual-cognitive mechanisms, are assumed to overlap those of perceiving multiple elements in complex and dynamic situations of everyday life (Faubert and Sidebottom 2012; Romeas et al. 2016, 2025). As initial evidence in laboratory conditions, aircraft radar operators (Allen et al. 2004), and regular players of action video games (Green and Bavelier 2006) demonstrated higher visual tracking performance compared to people who were not involved in daily attentionally demanding activities. Furthermore, complementary results show visual tracking improvement in non-video game players after video game training (Green and Bavelier 2006), which demonstrates that visual tracking is not only developed through age (Trick et al. 2005a, b; Sekuler et al. 2008; Brockhoff et al. 2016) but may also be enhanced after regular exposure to a visually demanding environment. Regarding sports activities (Liu et al. 2024), enhanced visual tracking ability is reported in players of team sports (Harris et al. 2020), such as volleyball (Zhang et al. 2009), basketball (Qiu et al. 2018; Jin et al. 2020) and rugby union (Martín et al. 2017), compared to non-team sport players. This advantage appears to be mitigated by playing position (Martín et al. 2017), gender (Legault et al. 2022; Jin et al. 2023), and competition level (Qiu et al. 2018; Liu et al. 2024).

Nevertheless, concerns are raised about the ecological validity of traditional visual tracking tasks to examine the relationship between visual attention and sports expertise, i.e., with participants in a fixed position to track randomly moving objects on a frontal plane (Meyerhoff et al. 2017). Literature suggests that the effect of expertise in laboratory conditions is moderated by the match between the constraints of the assessment setup and those of the field (Mann et al. 2007). This mismatch of visual constraints has been addressed in two different ways. On the one hand, the visual tracking task can be displayed on a large, curved projection dome to stimulate a wider peripheral field (Franconeri et al. 2008). In that vein, Ehmann and colleagues developed a 360° MOT-based task to approximate the constraints of gathering information about surrounding visual cues in soccer (2022). On the other hand, a Multiple-Players-Tracking task (MPT) has been proposed to integrate the visual tracking task in a soccer player's viewpoint on a virtual field, with game simulations animated by players trajectories from real matches (Vu et al. 2022). Respectively, visual tracking differences were reported between young players of different competition levels (Ehmann et al. 2022) and between soccer and non-soccer players (Vu et al. 2022).

The MPT version aimed to enhance the representative-ness of visualization constraints by leveraging Virtual Reality (VR) technologies (Vu et al. 2022). Head-mounted

displays were used to provide participants with an adaptive point of view, dynamically updating the rendering of the synthetic virtual environment in response to their movements. Participants could therefore move their bodies and heads naturally to explore their surroundings. Nevertheless, participants were only required to monitor the movements of teammates or opponents along realistic trajectories, as no virtual ball was included in the visual tracking task. This differs from real game situations, where soccer players must perceive their surroundings while running and interacting with the ball. Considering this multitasking aspect of soccer could be another avenue for enhancing the representative-ness of the visual tracking tasks.

In a resource-based theory of attention (Wickens 2002), even highly automated motor tasks, such as standing or walking, require attentional resources, and interference may occur with perceptual-cognitive activities (Woollacott and Shumway-Cook 2002). Multitasking may be investigated through dual-task methodology (Huang and Mercer 2001), which involves solving a secondary task in the time course of a primary task. Interference, or dual-task cost, is reflected by the performance decrement from single to dual-task condition. Literature shows a decrease in performance in street crossing scenarios, in which decision-making also requires attention to multiple moving objects, when a concurrent, attentionally demanding, task must be performed simultaneously, such as listening to music or chatting (Neider et al. 2011). Concerning visual tracking ability, a decrease in tracking performance has been observed when a secondary visual task had to be performed simultaneously (e.g., a digit categorization task (Allen et al. 2004), or when participants were either actively or passively moving (Thomas and Seiffert 2010)). Cognitive-motor interference was also illustrated by a decrease in postural stability (Fabri et al. 2017; Van Biesen et al. 2017), walking speed (Pothier et al. 2014) and vertical jump height (Fleddermann and Zentgraf 2018) when a visual tracking task was simultaneously required. Furthermore, Pothier and colleagues' results revealed that the interference is moderated by the age of participants, to the disadvantage of older adults (2014). Van Biesen and colleagues also reported that athletes with intellectual disabilities were more affected by the dual-task than control athletes in both a visual tracking task and a single leg balance test (2017). How attention can be properly divided between two concurrent tasks seems to depend on individual perceptual-cognitive characteristics. Beyond age and intellectual impairment, expertise could be another moderating factor for dual-task interference on visual tracking (Moreira et al. 2021). Gabbett and colleagues reported differences between high-skilled and less-skilled rugby players in a specific decision-making task only in a dual-task condition, but not in a single-task condition (2011). The authors argued
that the attentional demands of a specific ability decrease as expertise increases, allowing for simultaneous task completion (Gabbett et al. 2011). However, there is still no evidence that the visual tracking of high-skilled team sports players is more resilient to the attentional interference of the dual-task than that of less-skilled players.

The current study adapts the MPT version of the visual tracking task developed by Vu et al. (2022) to simulate backward passing situations in VR. Here, from the perspective of the ball receiver, participants engage in two tasks inherent to soccer: monitoring teammates and opponents while receiving a virtual ball. With defenders and goalkeepers increasingly involved in the ball's circulation, these situations are frequent in a game and must be well managed to avoid losing the ball, or even gaining an advantage over the opposing team. As the situations are experienced from a rear viewpoint on the virtual field, and a reception task needs to be performed, the environment is displayed in a Cave Automatic Virtual Environment (CAVE). This facility ensures participants a wide field of view to perceive all the virtual players, a space large enough to move and explore the virtual scene as on a real pitch, and a visualization of their own body to regulate their movement in order to interact with a virtual ball. The main objective is to analyze how motor interaction with the ball influences the visual tracking performance of soccer players, and whether this effect is modulated by their level of competition. Compared to lower-level players, higher-level soccer players are expected to show a smaller decrease in visual tracking performance induced by the simultaneous reception of a virtual ball. This could be associated with a reduced attentional cost in ball reception, visual tracking, or both tasks, or even with a more efficient allocation of attentional resources between the two. As part of an exploratory analysis, a complementary objective is to observe the spontaneous motor behavior of participants during this exercise. On the one hand, visual tracking activity can be affected by self-motion (Thomas and Seiffert 2010). On the other hand, embodying the role of a player to interact with the ball may encourage participants to move within the CAVE. Therefore, global displacement is measured to assess how the presence and interaction with the ball may affect participants' behavior. An increase in global displacement due to position regulation relative to the ball would suggest greater ecological validity of the task, as soccer players are almost constantly in motion during a real game (Taylor et al. 2017).

# 2 Methods

## 2.1 Participants

Twenty-four males (26±8 years old) soccer players, including goalkeepers ($n=21$) and central defenders ($n=3$), with normal or corrected-to-normal vision, were included in this study. Sample size was estimated using G\*Power 3.1

(Faul et al. 2007), assuming an effect size of 0.1 ($\eta_p^2$) for an interaction between skill groups and difficulty conditions, a statistical power of 0.90, and an alpha probability error of 0.05, which yielded a required sample of 11 participants per group. Twelve players competing in French national leagues were assigned to a *High-Skilled group* (HS) and the other 12 competing in French regional or district leagues formed the *Less-Skilled group* (LS). In accordance with the Declaration of Helsinki, written consent was obtained prior to participation. The experimental protocol and the data collection procedure were validated beforehand by a French national ethics committee in sports sciences (IRB00012476-2021-16-04-102).

## 2.2 Materials

A 12 m x 4 m x 4 m CAVE was used to display the immersive virtual environment (Fig. 1). As there was no left wall in this CAVE, the origin of the virtual environment was shifted slightly to the right (about 2 m) of the CAVE mid-point. This ensures that projections on the front and right walls and on the floor cover a participant's entire field of view. A 16-camera Vicon system was used to capture the movements of VR goggles, a hand controller, and passive clusters of markers on the participant's ankles. The VR goggles allowed stereoscopic and adaptive perception of the virtual environment. The hand controller was used for the selection phase of the visual tracking task (see next section). And the passive clusters of markers were used to collocate the user's feet in the virtual environment for the reception task (see next section). The positions of the feet were represented in the virtual environment using 30 cm-long capsules. The virtual environment was generated with Unity engine and the synchronization across projection hardware was ensured by MiddleVR middleware. The virtual environment represented the “*Stade de France*”, with 21 virtual players modeled as simplified mannequins. Eleven virtual opponents were represented in blue, and 10 virtual teammates were represented in red (Fig. 1).
<figure><img src="image_2.png" /><figcaption>Fig. 1 The visual tracking task during soccer backward passing situations simulated in a CAVE</figcaption></figure>

## 2.3 Simulated soccer situations

Twelve backward passing situations were extracted from real games to simulate soccer situations. The trajectories of the ball and the 22 players on the field were obtained from professional French Ligue 1 championship<fnref n="1" />. The situations lasted 10 s, and the backward pass occurred between the seventh and ninth seconds. The participant's role was that of the ball receiver in a backward position of the field, such as a goalkeeper or a central defender. Participants had to start each trial at a location indicated by a gray circle on the floor (Fig. 1) but were then free to move around the CAVE floor. From this position, each virtual player could be seen during the situation. The 12 open-play situations were selected to ensure similar spatiotemporal features, particularly with players scattered across the width of the field. Considering an imaginary division of the width of the field into 3 equal lanes, at least 2 teammates and 2 opponents should be in each lane. The location of the teammate making the backward passes was evenly distributed among the 3 lanes in the 12 selected situations.

## 2.4 Tasks

### 2.4.1 Visual tracking task

Three target-players had to be monitored for a complete situation (10 s). The target-players were either 1 teammate and 2 opponents or vice versa. In each situation, the three target-players were chosen by the experimenters in such a way that a target-player was located in each of the 3 imaginary lanes in width of the field at the beginning and end of the trial (participants were not aware of this information). This ensures that all participants faced the same level of difficulty. At the beginning of a situation, all virtual players were stationary, the 3 target-players were highlighted and pointed with an arrow until the trial started. Then each virtual player moved for 10 s and stopped. At the end of each trial, participants selected the 3 target-players by pointing the hand controller. The number of correct answers was recorded.

### 2.4.2 Reception task

On each trial of the reception task, a virtual teammate (red mannequin) initiated a pass to the participant, who had to

<footnote n="1">¹ StatsBomb, “Football Like Never Before,” https://statsbomb.com.</footnote>
receive the ball by intercepting its trajectory with one foot. The virtual ball stopped if a collision was detected with the collocated virtual capsule representing the foot in the virtual environment. The direction of the pass was generated based on the participant's initial position in the virtual environment, so that no reception occurred if the participant did not move. The ball passed to the left or right, from 0.8 to 1.2 m, of the participant respectively in half of the situations.

## 2.5 Conditions

Participants were presented with four conditions, each randomized across trials. First, the 12 selected situations were replicated in two experimental conditions. In a *dual-task condition* (DT-TrackPass), participants were required to complete both the visual tracking task and the reception task (see Video 1 in supplementary material). In a *single tracking condition* (ST-Track), participants exclusively engaged in the visual tracking task without the presence of the virtual ball. Second, two control conditions were introduced, in which the 12 simulated situations were mirrored along the longitudinal axis of the field to prevent participants from recognizing them (Makovski et al. 2008). In a single reception task (ST-Pass) condition, participants were required solely to intercept the virtual ball trajectory. This control condition was used to check participants' ability to interact with the virtual ball and measure intuitive displacements during the reception task. In a tracking-no-pass condition (TrackNoPass), the virtual ball remained in the environment, but no pass occurred in the direction of the participant during the visual tracking task. As participants were unaware of whether a pass would occur or not in both DT-TrackPass and TrackNoPass conditions, this latter condition aimed to control the impact of tracking an additional object, the ball, along with the 3 virtual target-players.

## 2.6 Procedure

Upon arrival, participants were informed of the research goals and were equipped with the VR goggles, hand controller, and passive clusters of markers. Participants initially performed 4 practice trials, which were not recorded, to become familiar with the proposed tasks. Afterward, participants completed the 48 trials presented in a random order (12 situations in 4 conditions), for roughly 30 min.

<table><caption>**Table 1** Visual tracking performance (mean±standard deviation) for each group across ST-Track, TrackNoPass and DT-TrackPass conditions</caption><thead><tr><th></th><th>ST-Track</th><th>TrackNoPass</th><th>DT-TrackPass</th></tr></thead><tbody><tr><td>HS</td><td>2.62±0.17</td><td>2.21±0.39</td><td>2.08±0.28</td></tr><tr><td>LS</td><td>2.60±0.37</td><td>2.29±0.37</td><td>2.23±0.31</td></tr></tbody></table>

## 2.7 Data analysis

The visual tracking performance was measured as the number of successfully tracked target-players in each condition. The global distance covered during a trial was measured as the cumulative displacement of the head on the horizontal plane. Both variables were averaged in each condition. Two mixed-model ANOVAs were used to observe the between-subjects effect of the group of participants, the within-subjects effect of experimental condition and their interaction effect on visual tracking performance and covered distance respectively. Performance in the reception task on each trial was measured as a binary variable: success or failure. Chi-square tests of independence were performed to determine whether the reception result was associated with the experimental condition (ST-Pass/DT-TrackPass) or the group of participants (HS/LS). Where appropriate, pairwise t-tests with Bonferroni correction were performed for post hoc analysis. Cohen's *d* was computed to measure effect size in pairwise comparisons, with a positive value indicating that the first element of the comparison is greater than the second, and vice versa. Normality, equivariance, and sphericity were verified by the Shapiro-Wilk, Levene, and Mauchly tests, respectively. Greenhouse-Geisser correction was applied when needed. Statistical analysis was run on R 4.3.2. Anonymized data—including averaged data for each subject and condition (tracking accuracy, covered distance, reception success)—can be found in the supplementary material (Supp. 2). The R code used for the statistical analyses can be provided upon reasonable request, to qualified researchers, via email to the corresponding author.

# 3 Results

## 3.1 Visual tracking performance

The visual tracking performance (mean±standard deviation) for each group across ST-Track, TrackNoPass and DT-TrackPass conditions is reported in Table 1. The mixed-model ANOVA revealed a significant main effect of experimental condition, $F(2,44)=30.64, p<.001$, $\eta_p^2=0.58$, but no significant main effect of group, $F(1,22)=0.39, p=.540$, $\eta_p^2=0.02$, and no significant interaction effect, $F(2,44)=0.96, p=.392$, $\eta_p^2=0.04$, on visual tracking performance. As illustrated in Fig. 2, post-hoc tests revealed a higher visual tracking performance in ST-Track (2.61±0.28) compared to TrackNoPass (2.25±0.37), $t(23)=-6.08, p<.001, d=1.24$, and compared to DT-TrackPass (2.16±0.30), $t(23)=-7.93, p<.001, d=1.62$, but no difference between TrackNoPass and DT-TrackPass, $t(23)=1.42, p=.510, d=0.29$.
<figure><img src="image_3.png" /><figcaption>Fig. 2 Visual tracking performance by group in the three experimental conditions (****: p-value<0.0001)</figcaption></figure>

## 3.2 Reception performance

Only 2% of all trials with reception (ST-Pass and DT-Track-Pass) from all participants were missed. Nevertheless, chi-square tests of independence were performed to see whether performance on the reception task (success/failure) was associated with the experimental condition (ST-Pass/DT-TrackPass) or the group of participants (HS/LS). The tests of independence revealed that the success in the reception task was not significantly associated with the experimental condition, $\chi^2(1)=0.79, p=.375$, nor with the group of participants, $\chi^2(1)=0.08, p=.770$.

## 3.3 Covered distance

The mixed-model ANOVA revealed a significant main effect of experimental condition, $F(3,66)=66.91, p<.001$,

$\eta_p^2=0.75$, but no significant main effect of group, $F(1,22)=0.003, p=.958$, $\eta_p^2<0.01$, and no significant interaction effect, $F(3,66)=0.06, p=.981$, $\eta_p^2<0.01$, on covered distance. As illustrated in Fig. 3, post-hoc tests revealed a higher covered distance in ST-Pass (2.95 m±0.80) compared to TrackNoPass (2.38 m±0.96) and DT-TrackPass (2.45 m±0.54). No difference was revealed between TrackNoPass and DT-TrackPass, but the covered distance in both conditions was higher than in ST-Track (1.26 m±0.72). All pairwise statistics are reported in Table 2.

# 4 Discussion

Backward passing situations, experienced from the receiver's perspective, were simulated in VR. The goal of this study was to observe the ability of soccer players to allocate
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Covered distance by group in the four experimental conditions (***: *p*-value<0.001 and ****: *p*-value<0.0001)</figcaption></figure>

<table><caption>**Table 2** Post-hoc statistics computed between condition levels for covered distance</caption><thead><tr><td>Comparison</td><td>Pairwise statistics</td></tr></thead><tbody><tr><td>ST-Track vs. TrackNoPass</td><td>t(23)=-8.79, p<.001, d=-1.79</td></tr><tr><td>ST-Track vs. DT-TrackPass</td><td>t(23)=12.02, p<.001, d=-2.45</td></tr><tr><td>ST-Track vs. ST-Pass</td><td>t(23)=11.31, p<.001, d=-2.31</td></tr><tr><td>TrackNoPass vs. DT-TrackPass</td><td>t(23)=0.66, p=.512, d=-0.14</td></tr><tr><td>TrackNoPass vs. ST-Pass</td><td>t(23)=4.56, p<.001, d=-0.93</td></tr><tr><td>DT-TrackPass vs. ST-Pass</td><td>t(23)=-4.88, p<.001, d=-1.00</td></tr></tbody></table>

their visual attention between a perceptual-cognitive task and a soccer-specific motor task. The experimental manipulations investigated the effects of two extrinsic factors, ball presence and ball reception, and one intrinsic factor, the competition level, on visual tracking performance and covered distance of soccer players.

## 4.1 Virtual ball presence effect

The decrease in visual tracking performance associated with the presence of the virtual ball can be explained by several factors. First, the difficulty of the task increases with the number of targets to be tracked (Qiu et al. 2018; Jin et al.

2020). Visual tracking performance may have been affected by the addition of an extra element to track, the ball. Second, the addition of another visual element can increase the density of the scene or add a distracting effect (Bettencourt and Somers 2009). For instance, even irrelevant virtual elements, such as a referee, could impair tracking performance. Third, remaining aware of a possible upcoming pass requires visual attentional resources that overlap with those used for visual tracking (Allen et al. 2004; Romeas et al. 2019). Participants also covered greater distances in conditions where the ball was present (TrackNoPass, DT-TrackPass, and ST-Pass), suggesting they adapted their position in anticipation of a pass. While self-motion can impair visual tracking (Thomas and Seiffert 2010), this voluntary movement may enhance ecological validity by replicating actual game conditions. Even though motor reception was not required in the TrackNoPass condition, the possibility of stepping into the situation encouraged participants to remain in motion. In a real game, soccer players are rarely static; they are constantly moving, even at low speeds. Importantly, the task design did not impose movement directly (e.g., “you must move at X m s⁻¹ for Y s”) but instead encouraged
behaviors—such as balancing movement and stability—that naturally emerge with expertise in practice. These findings suggest that VR simulations may enhance the representativeness of off-field testing or training by replicating similar visual constraints and eliciting intuitive behaviors. Still, in conditions involving the visual tracking task where the ball was present (TrackNoPass and DT-TrackPass), the covered distance was shorter than in the single reception task. Participants likely sought a compromise between dynamism to prepare for the ball reception and stability to perform the visual tracking task.

## 4.2 Virtual ball reception effect

No additional decrement in visual tracking performance was observed when motor execution was required to receive a virtual ball (DT-TrackPass condition compared to TrackNoPass). This finding was unexpected, as visual tracking impairments have been observed in badminton players simultaneously engaged in a virtual birdie interception task (Romeas et al. 2019). The implemented backward pass situations were probably not challenging enough to alter the visual tracking performance of the soccer players. Further research could manipulate interception constraints—such as ball crossing distance and speed—to identify the specific circumstances in which motor execution interferes with visual tracking, which may differ across individuals. For instance, increasing the ball crossing distance would require participants to cover a longer distance, which was not significantly different between the TrackNoPass and DT-TrackPass conditions in this study. Also, the average ball velocity during the reception task was $18.37 \pm 1.92 \text{ m·s}^{-1}$ in this study, which is consistent with regular passing actions in soccer. Attentional load is expected to increase with ball speed, as participants would need to visually track the ball (Franconeri et al. 2008) and to receive it. While our findings underscore distinct attentional challenges associated with ball presence, translating these into VR training requires caution: current evidence for transfer to actual soccer performance is limited, and VR systems cannot yet replicate critical elements such as realistic ball contact and multi-sensory feedback. In this study, the ball stopped upon collision with the participant's foot, allowing only a binary reception outcome and simplified motor gestures, which may have underestimated dual-task costs. More accurate ball behavior modeling would enable examination of interference not only on perceptual tracking but also on motor performance (Schaefer 2014; Fleddermann and Zentgraf 2018), and could reveal whether higher-skill players—with less effortful soccer-specific actions—are indeed less affected by dual-task decrements. Future research should first validate

the ecological transfer of these VR findings before formulating specific training recommendations.

## 4.3 Competition level effect

Contrary to expectations and the sports science literature (Faubert 2013; Qiu et al. 2018; Ehmann et al. 2022; Liu et al. 2024), soccer players competing at higher levels did not outperform lower level players in visual tracking. The relationship between soccer expertise and the perceptual-cognitive skills involved in the visual tracking task is more complex than initially assumed. Although this MPT-version aimed to increase the representativeness of task constraints, by incorporating realistic viewpoints and virtual player trajectories, the perceptual-cognitive skills involved likely still differ too substantially from those that distinguish expertise in soccer. Although reasonably closely aligned with real-life constraints, the proposed task did not fully capture the attentional demands of soccer. The demands of the task diverged from soccer in that the targets to be tracked were not determined by the demands of the game situation, but were instead selected in advance by the experimenters, remained the same over time, and were neither relevant nor usable for decision-making in a given situation. As opportunities to act emerge and disappear in the unpredictable dynamics of soccer, performance requires not only continuously attending to multiple elements to make appropriate decisions, but also adapting the selection of these elements as their informativeness changes over time. From a perception-action perspective, players' exploration of their surroundings to pick up task-relevant information may partly explain their success in decision making with the ball (Jordet et al. 2020). In this tracking task, virtual teammates and opponents selected as targets did not offer reliable informational cues for perceiving affordances related to passing, dribbling, or clearing actions. Furthermore, in this particular context, the visual tracking task did not support the execution of a soccer-specific action, and the reception task did not reinvest the information gathered during the visual tracking. This decoupling between the two tasks may probably induce unnatural or counter-intuitive experiences in participants. Further experiments could build on paradigms linking attention to action to address this point, such as the Multiple-Object Avoidance (MOA) task, which requires continuous control of an object to prevent collisions with multiple hazards, resembling the demands observed in driving contexts (Mackenzie et al. 2022).

Given that expertise effects had already been observed in visual tracking studies (Liu et al. 2024), another explanation lies in the lack of difficulty of the task (Qiu et al. 2018). For an oversimplified task, less-skilled athletes may show
similar success as high-skilled athletes in visual tracking by exerting more effort.

## 4.4 Limits

Some limitations regarding the current study should be acknowledged. First, the current sample is restricted to male adults playing at back positions, which limits the representativeness and generalizability of the findings. Considering other game scenarios with different playing positions (Martín et al. 2017), younger or older participants (Ehmann et al. 2022), and female participants (Jin et al. 2023) would help overcome this limitation in future experiments. Second, the game data used to simulate the soccer situations contained only the trajectory of the players and the ball, but not their orientation and rotation respectively. Therefore, only simplified mannequins without a humanoid skeleton and a white ball without texture were implemented in the virtual environment. This deprived participants of important visual cues such as body orientation and posture which are critical for anticipation (North et al. 2016), and may explain the absence of differences in visual tracking performance between the two groups of participants. Nevertheless, reproducing soccer-specific locomotor and technical movements in a natural manner would have been challenging, as skeletal data were not available in our motion dataset for animating virtual players. Therefore, this choice aimed to minimize the risk of participants rejecting the virtual experience or altering their performance due to inaccurate or unrealistic simulated skeletal movements. In addition, previous research has shown that representing players as single points, rather than skeletal humanoids, is sufficient (albeit less informative) to perceive collective movement patterns for anticipation in soccer (North et al. 2016). Then, the lack of ball texture is expected to have only a minimal effect on reception (albeit not negligible), since the ball followed a straight path when reception was required, rather than a curved trajectory, which would have been more problematic (Casanova et al. 2015). When 3D skeletal data and ball behavior become easier to collect or can be estimated more accurately, integrating these data would help improve the representativeness of future experiments. In this perspective, the development of markerless motion-capture systems based on computer vision offers the opportunity to extract 3D skeletal data from broadcast game footage, which could be used to populate future virtual scenes (Jiang et al. 2024). Third, only the covered distance during each trial was analyzed as a behavioral variable to describe how participants manage the current dual-task scenario. Effective gaze control is typically required in both visual tracking (Hyönä et al. 2019) and interception (Navia et al. 2017). Visual exploration behaviors can be examined through the analysis of

eye, head, and body movements. Although head orientation, which is a valuable measure for analyzing visual exploration in soccer players both in real games (Jordet et al. 2020) and in VR (Rojas Ferrer et al. 2020; Wirth et al. 2021), was recorded, a reliable analysis procedure could not be implemented in the current study.

## 5 Conclusion

This study used VR to integrate a visual tracking task within simulated soccer situations, aiming to evaluate the visual attention of soccer players under representative conditions. Allowing interaction with the ball increased participant movement, introducing constraints similar to those in real game situations when perceiving teammates and opponents. However, the effect of expertise on visual tracking in this context remains inconclusive. Future research should implement more challenging and ecologically valid dual-task conditions to clarify the influence of ball reception on visual tracking performance and better differentiate between skill levels.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10055-025-01310-1.

**Acknowledgements** This work was supported by the Region Bretagne, by the National Research Agency under the Investments for the Future program in France 2030 with the grants ANR-18-EURE-0022 (DIGIS-PORT) and ANR-21-ESRE-0030 (CONTINUUM).

**Author contributions** Conceptualization and Methodology: Vu, Sorel and Kulpa; Formal analysis, Investigation and Writing – original draft preparation: Vu; Writing – review and editing: Vu, Sorel and Kulpa; Funding acquisition: Bideau and Kulpa; Resources and Supervision: Sorel, Bideau and Kulpa.

**Data availability** Anonymized data—including averaged data for each subject and condition (tracking accuracy, covered distance, interception success)—can be found in the supplementary material (Supp. 2).

### Declarations

**Competing interests** The authors declare no competing interests.

**Ethical standards** The authors have declared that no conflict of interest exists. In accordance with the Declaration of Helsinki, written consent was obtained prior to participation. The experimental protocol and the data collection procedure were validated beforehand by a French national ethics committee in sports sciences (IRB00012476-2021-16-04-102).

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