# Discriminating sensorimotor performance in elite football players: a comparative study of reactive and planned agility

Nolwenn Poquerusse, Nicolas Vignais, Rufin Boumpoutou, Benoît Bideau, Anthony Sorel

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## To cite this version:

Nolwenn Poquerusse, Nicolas Vignais, Rufin Boumpoutou, Benoît Bideau, Anthony Sorel. Discriminating sensorimotor performance in elite football players: a comparative study of reactive and planned agility. Multidisciplinary Biomechanics Journal, 2025, 2, pp.505-507. 10.46298/mbj.16199. hal-05191752v2

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Submitted on 23 Oct 2025

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# Discriminating sensorimotor performance in elite football players: a comparative study of reactive and planned agility

Nolwenn Poquerusse<sup>a*</sup>, Nicolas Vignais<sup>a</sup>,
Rufin Boumpoutou<sup>b</sup>, Benoît Bideau<sup>a</sup>, Anthony Sorel<sup>a</sup>

<sup>a</sup> Univ Rennes, Univ Rennes 2, Inria, M2S, 35000, Rennes, France

<sup>b</sup> Rennes Ortho Sport, Polyclinique Saint Laurent, Rennes, France

* Corresponding author: nolwenn-poquerusse@etudiant.univ-rennes2.fr

Received date: 06/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** agility, soccer, sensorimotor, performance

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

In sports with high uncertainty and constantly evolving environments like soccer, athletes must continuously process visual cues and adjust their movements. A critical skill is reactive agility, the ability to rapidly change velocity or direction in response to an external stimulus (Sheppard et al., 2006). Unlike planned agility, which reflects physical conditioning, reactive agility involves perceptual-cognitive processes, decision-making, and motor execution, without knowing the stimulus-driven direction in advance.

Most studies have focused on biomechanical variables related to planned agility (Loturco et al., 2019), with fewer investigating reactive agility protocols using video-based stimuli or sport-specific actions (Inglis et al., 2016). There remains a need for a standardized controlled test evaluating both agility types through realistic movement patterns.

This study introduces a novel protocol for evaluating both planned and reactive agility under standardized conditions. We hypothesize that this protocol can reveal the distinct nature of reactive and planned agility in a population of elite soccer players.

## 2. Methods

### 2.1 Subjects

27 elite professional soccer players (mean ± SD: weight, 77.5 ± 11 kg; age, 23.0 ± pm 4.3 years) from the French Ligue 1 Championship took part in the study. All met

inclusion criteria for high-level play and provided written informed consent.

### 2.2 Materials

The setup included two force platforms (AMTI, Watertown, MA, USA) recording at 1000 Hz. Players wore 45 retroreflective markers placed on anatomical bony landmarks. A 23-camera optoelectronic system (Qualisys, Stockholm, Sweden) recorded 3D marker positions at 200 Hz to compute kinematics.

### 2.3 Procedure

Participants were instructed to sprint toward one of four targets located at each corner of a 6-meter square, starting with a jump on central force platforms. Targets were displayed on a front screen under two anticipation conditions: either before the jump (planned condition), allowing the player to plan his motor action in advance, or 0.2s after takeoff (reactive condition), forcing him to react to an uncertain situation. Each player performed 24 randomized trials (three per condition and target).

### 2.3 Data analysis

Kinematic data were calculated from two key events: the landing (when the force platforms registered a force exceeding 10 N) and the target-reaching (when the center of mass trajectory was within 2 meters from the target). The two analyzed variables were the target
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Scheme of the physical environment of the reactive agility protocol.</figcaption></figure>

reaching time, defined as the interval between these phases, and the curvilinear distance traveled, the distance covered by the subject's center of mass along a curvilinear path. Averages were computed over three trials per condition.

Student's t-test was conducted to compare the variables across agility conditions. Athletes were classified into distinct groups using a k-means clustering algorithm based on their agility times to reach the target under the reactive condition. The optimal number of clusters (k = 3) was determined using the elbow method. A one-way ANOVA was performed to examine differences in agility performance across the identified clusters.

## 3. Results and discussion

Student's t-test analysis revealed significant differences in both reaching times and curvilinear distances between reactive and planned conditions (*p* < 0.001). This supports previous findings on discriminating player performance based on reactivity but contrasts with Morland and al. (2013) who did not identify a significant difference in a reactive agility test involving visual stimuli. K-means cluster analysis categorized athletes as: *Very Reactive* (*times* < 0.93 *s*, *n* = 8), *Moderately Reactive* (*times* between 0.93 *s* and 1.21 *s*, *n* = 11), and *Less Reactive* (*times* > 1.21 *s*, *n* = 8).

ANOVA on planned condition reaching times revealed significant differences between the three

reactivity-based groups (*p* = 0.010, η² = 0.316). Post-hoc analysis revealed that this was specifically between the *Very Reactive* and the *Less Reactive* groups (*p*<sub>tukey</sub> = 0.008, *d* = 1.648). This supports our hypothesis and aligns with those of Young et al. (2010) suggesting agility skills differ the two conditions.

<table><caption>**Table 1.** Average target reaching times and curvilinear distances according to running direction, in planned (P) and reactive (R) conditions.</caption><thead><tr><th>Direction</th><th>Condition</th><th>Time SD (s)</th><th>Distance SD(m)</th></tr></thead><tbody><tr><td rowspan="4">BL</td><td rowspan="2">P</td><td>0.88</td><td>2.15</td></tr><tr><td>±0.09</td><td>±0.08</td></tr><tr><td rowspan="2">R</td><td>1.15†</td><td>2.19†</td></tr><tr><td>±0.13</td><td>±0.09</td></tr><tr><td rowspan="4">BR</td><td rowspan="2">P</td><td>0.88</td><td>2.12</td></tr><tr><td>±0.10</td><td>±0.08</td></tr><tr><td rowspan="2">R</td><td>1.09†</td><td>2.17†</td></tr><tr><td>±0.11</td><td>±0.09</td></tr><tr><td rowspan="4">FL</td><td rowspan="2">P</td><td>0.78</td><td>2.01</td></tr><tr><td>±0.08</td><td>±0.04</td></tr><tr><td rowspan="2">R</td><td>1.02†</td><td>2.05†</td></tr><tr><td>±0.15</td><td>±0.07</td></tr><tr><td rowspan="4">FR</td><td rowspan="2">P</td><td>0.80</td><td>2.01</td></tr><tr><td>±0.07</td><td>±0.05</td></tr><tr><td rowspan="2">R</td><td>1.04†</td><td>2.03†</td></tr><tr><td>±0.17</td><td>±0.09</td></tr></tbody></table>

With BL: backward left, BR: backward right, FL: forward left and FR: forward right. † means values R>P with p<0.001.

The effect size for reactive agility was large (η² = 0.813), compared to a moderate effect for planned agility (η² = 0.316). This reveals the distinct nature of reactive and planned agility. It highlights the importance of investigating the biomechanical and neuromuscular determinants specific to reactivity, beyond the mechanics of simple change of direction.

## 4. Conclusion

This study introduces a new protocol to assess both reactive and planned agility in elite football players. The results highlight that these two forms of agility rely on distinct performance determinants. By emphasizing reactive agility, the protocol enables a more specific evaluation of sensorimotor capacities, supporting its relevance as a key factor in high-level performance.
**Conflict of Interest Statement**

The authors declare no conflicts of interest.

**Contributor Roles**

NP: Formal analysis, Data curation, Software, Visualization, Writing original draft; NV: Conceptualization, Project administration, Supervision, Writing-review & editing; RB: Conceptualization; BB: Funding acquisition, Project administration, Supervision; AS: Conceptualization, Methodology, Funding acquisition, Investigation, Project administration, Supervision, Writing-review.

**Funding**

This study was partially funded by the French National Research Agency within the framework of the France 2030 program for EUR DIGISPORT (ANR-18-EURE-0022) and CONTINUUM (ANR-21-ESRE-0030) projects.

**References**

Inglis, P., & Bird, S. (2016). Reactive agility tests: Review and practical applications. *Journal of Australian Strength and Conditioning*, 24, 57–64. https://www.strengthandconditioning.org/jasc-24-5/114-review-of-the-literature-reactive-agility-tests-review-and-practical-applications

Loturco, I., A. Pereira, L., T. Freitas, T., E. Alcaraz, P., Zanetti, V., Bishop, C., & Jeffreys, I. (2019). Maximum acceleration performance of professional soccer players in linear sprints: Is there a direct connection with change-of-direction ability? *Plos one*, 14(5), e0216806. https://doi.org/10.1371/journal.pone.0216806

Morland, B., Bottoms, L., Sinclair, J., & Bourne, N. (2013). Can change of direction speed and reactive agility differentiate female hockey players? *International Journal of Performance Analysis in Sport*, 13. https://doi.org/10.1080/24748668.2013.11868666

Sheppard, J. M., Young, W. B., Doyle, T. L. A., Sheppard, T. A., & Newton, R. U. (2006). An evaluation of a new test of reactive agility and its relationship to sprint speed and change of direction speed. *Journal of Science and Medicine in Sport*, 9(4), 342–349. https://doi.org/10.1016/j.jsams.2006.05.019

Young, W. B., & Willey, B. (2010). Analysis of a reactive agility field test. *Journal of Science and Medicine in Sport*, 13(3), 376-378. https://doi.org/10.1016/j.jsams.2009.05.006