Assessing the performance of soccer goalkeepers based on their cognitive and motor skills

Rodríguez-Arce, Jorge; Flores-Núñez, Laura I.; Portillo-Rodríguez, Otniel; Hernández-López, Saúl E.

doi:10.1080/24748668.2019.1647042

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…Separate tests were conducted for naïve participants and skilled goalkeepers to directly address the generalizability of previous catching research to practical scenarios (i.e., for naïve participants) and the potential practical implications (i.e., for skilled goalkeepers). In addition, we tested the expectation that the performance of skilled goalkeepers would be affected less by the presence of the wall than that of naïve participants, since they would be able to capitalize on their advanced visual [ 19 ], motor [ 20 ] and visuomotor [ 21 , 22 ] capabilities once the ball comes into view from behind the wall. Other than this, our study was not designed as a traditional expert-novice paradigm, so no further group comparisons were planned.…”

Section: Introductionmentioning

confidence: 99%

A goalkeeper’s performance in stopping free kicks reduces when the defensive wall blocks their initial view of the ball

et al. 2020

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Free kicks are an important goal scoring opportunity in football. It is an unwritten rule that the goalkeeper places a wall of defending players with the aim of making scoring harder for the attacking team. However, the defensive wall can occlude the movements of the kicker, as well as the initial part of the ball trajectory. Research on one-handed catching suggests that a ball coming into view later will likely delay movement initiation and possibly affect performance. Here, we used virtual reality to investigate the effect of the visual occlusion of the initial ball trajectory by the wall on the performance of naïve participants and skilled goalkeepers. We showed that movements were initiated significantly later when the wall was present, but not by the same amount as the duration of occlusion (~200ms, versus a movement delay of ~70-90ms); movements were thus initiated sooner after the ball came into view, based on less accumulated information. For both naïve participants and skilled goalkeepers this delayed initiation significantly affected performance (i.e., 3.6cm and 1.5cm larger spatial hand error, respectively, not differing significantly between the groups). These performance reductions were significantly larger for shorter flight times, reaching increased spatial errors of 4.5cm and 2.8cm for both groups, respectively. Further analyses showed that the wall-induced performance reduction did not differ significantly between free kicks with and without sideward curve. The wall influenced early movement biases, but only for free kicks with curve in the same direction as the required movement; these biases were away from the final ball position, thus hampering performance. Our results cannot suggest an all-out removal of the wall–this study only considered one potential downside–but should motivate goalkeepers to continuously evaluate whether placing a wall is their best option. This seems most pertinent when facing expert free kick takers for whom the wall does not act as a block (i.e., whose kicks consistently scale the wall).

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Section: Introductionmentioning

confidence: 99%

A goalkeeper’s performance in stopping free kicks reduces when the defensive wall blocks their initial view of the ball

et al. 2020

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“…All these theories provide a theoretical basis for indirectly measuring the body's energy consumption and controlling the intensity of the aerobic exercise by using exercise heart rate. Professional sports coaches will also use heart rate monitoring to determine the training intensity and amount of exercise of athletes, and adjust exercise training plans according to the physiological response of athletes to improve the effect of exercise training [16]. College students are the main force of colleges and universities.…”

Section: Related Workmentioning

confidence: 99%

Sports Energy Consumption Evaluation Based on Improved Adaptive Weighted Data Fusion Energy-Saving Algorithm

Han

Jiang

2022

Computational Intelligence and Neuroscience

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The purpose of this study is to use a portable cardiopulmonary function tester to measure the effect of sports smart bracelets and smartphone application software in monitoring the energy consumption of different types of physical activities, and to select several popular sports software in daily life for research. The tester is an accurate reference value. It compares the energy consumption monitoring effect and error percentage of several software in periodic exercise and discusses the relationship between the measured value and reference value of several software, so as to provide a scientific basis for exercise for the majority of athletes. The selection of software provides a reference and chooses a more suitable movement method according to its own actual situation to achieve the most objective and effective periodic movement. In this study, the CMA (constant modulus algorithm) is introduced into the decision feedback equalizer, and the CMA-DFE (decision feedback equalization) algorithm is formed. Since the CMA algorithm adopts a fixed step size, there is no way to solve the contradiction between the convergence speed and the steady-state residual error, so a constant modulus algorithm with a variable step size is proposed. The algorithm increases the step size in its initial stage to increase the convergence speed and reduces the step size after the algorithm converges to reduce the steady-state residual error. This study replaces the traditional CMA algorithm with the constant modulus algorithm with a variable step size. In this section, two basic algorithms for calculating the adaptive weighting factor are first proposed. Due to its limitations, a modified adaptive weighting factor is proposed. During normal running, the Huawei Band relatively accurately monitors the number of steps and energy consumption, and the motion software of the motion software relatively accurately monitors the distance. The monitoring of distance and energy consumption is relatively accurate in the Codoon sports software; in jogging, the Huawei Band is relatively accurate in monitoring the number of steps; the LeDong sports software is relatively accurate in monitoring distance; and the Codoon sports software in the energy consumption is relatively accurate.

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“…of how each motion component contributes to explaining a goalkeeper's kinematics during his motion [5], [6], for instance anticipate dive direction from the kinematics of a particular body segments [7]. The extracted insights can be used in designing injury avoiding strategies [6] or improve saving performance [8]- [10] by building a goalkeeper's biomechanical profile [11].…”

Section: Gheorghementioning

confidence: 99%

Less is More: End-to-end Learning of Insights from a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics

Lisca¹,

Prodaniuc²,

Grauschopf³

et al. 2021

Preprint

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<div>On-field sensor-based soccer player tracking solutions are emerging and provide new insights into the dynamics of the player during training or a match. Yet, not all player positions are equally privileged. Goalkeepers’ training and performance assess- ment were for a long time ignored. Understanding what is ”the side of the post where most high dives were performed” provides valuable input for both the trainer and the athlete to improve perfor- mance or avoid injuries. In the current study, we focus on a practical methodology to extract insights from goalkeeper kinematics to inform such analytics. We demonstrate that information from a single motion sensor can be successfully used for learning patterns in goalkeeper’s motion and provide an explainable goalkeeper kine- matics assessment. We employed raw and quaternions data and we evaluated a series of machine learning algorithms that discriminate dive types (i.e. binary classification) and dives from other types of specific motions (i.e. multi-class classification) directly from the data. Our results demonstrate that XGBoost outperforms other approaches in terms of performance when considering both raw and quaternions, essentially benefiting from both types of data. Additionally, each prediction of the model is accompanied by an explanation of how each sensed motion component contributes to describing a specific goalkeeper’s action captured by the model. The explainable predictions along with the efficient deployment of XGboost were decisive in our applied study. We evaluated our methodology on a first batch of experiments using online available data from 7 goalkeepers during 30 minutes-long training sessions.<br></div>

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Assessing the performance of soccer goalkeepers based on their cognitive and motor skills

Cited by 11 publications

References 19 publications

A goalkeeper’s performance in stopping free kicks reduces when the defensive wall blocks their initial view of the ball

A goalkeeper’s performance in stopping free kicks reduces when the defensive wall blocks their initial view of the ball

Sports Energy Consumption Evaluation Based on Improved Adaptive Weighted Data Fusion Energy-Saving Algorithm

Less is More: End-to-end Learning of Insights from a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics

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