Characterization of Static Balance Abilities in Elite Soccer Players by Playing Position and Age

Ibba, Gianfranco; Leban, Bruno; Scorcu, Marco

doi:10.1080/15438627.2014.944302

Cited by 38 publications

(37 citation statements)

References 25 publications

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“…On the other hand, our results were in agreement with previous studies, where authors hypothesized that there are significant differences in football players related to their physical characteristics and playing position (Pau, Ibba, Leban, & Scorcu, 2014;Romann & Fuchslocher, 2013). In this sense, players with good records of positioning and sprint speed tend to be, on average, classified as forwards, while other skills such as long and short passing are more specific of defenders.…”

Section: Discussionsupporting

confidence: 92%

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

Soto-Valero¹

2017

Rev. int. cienc. deporte

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The generation and availability of football data has increased considerably last decades, mostly due to its popularity and also because of technological advances. Gaussian mixture clustering models represents a novel approach to exploring and analyzing performance data in sports. In this paper, we use principal components analysis in conjunction with a model-based Gaussian clustering method with the purpose of characterizing professional football players. Our model approach is tested using 40 attributes from EA Sports' FIFA video game series system, corresponding to 7705 European players. Clustering results reveal a clear distinction among different performance indicators, representing four different roles in the team. Players were labeled according to these roles and a gradient tree boosting model was used for ranking attributes regarding to its importance. We found that the dribbling skill is the most discriminating variable among the different clustered players' profiles.Key words: association football; EA Sports' FIFA video game series system; machine learning; principal component analysis; Gaussian mixture clustering models; classification and regression trees. AbstractCorrespondencia/correspondence: César Soto-Valero Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Cuba Email: cesarsotovalero@gmail.com En las últimas décadas se ha visto un incremento considerable en la generación y disponibilidad de datos de fútbol, esto se debe fundamentalmente a la popularidad de este deporte así como a los avances tecnológicos realizados en este campo. Los modelos de agrupamiento basados en mixturas Gaussianas representan un enfoque novedoso para explorar y analizar datos de desempeño deportivo. En el presente trabajo, se lleva a cabo una caracterización de jugadores profesionales de fútbol utilizando técnicas de análisis de componentes principales y agrupamiento basados en mixturas Gaussianas. El modelo presentado es comprobado utilizando datos del sistema de videojuegos FIFA de EA Sports, dichos datos representan 40 atributos correspondientes a 7705 futbolistas europeos. Los resultados del agrupamiento revelan una clara distinción entre algunos indicadores de desempeño, los cuales corresponden a cuatro roles diferentes en el equipo. Consecuentemente, los jugadores fueron etiquetados de acuerdo a estos roles y un modelo de árboles de gradiente ampliado fue utilizado para ordenar los atributos de acuerdo a su importancia. Como resultado se identificó a la habilidad de driblear como la variable que mejor discrimina entre los diferentes perfiles de jugadores.Palabras clave: fútbol; sistema de videojuegos FIFA de EA Sports; aprendizaje automátic; análisis de componentes principales; agrupamiento basado en modelos de mixturas Gaussianas; árboles de clasificación y regresión. Resumen

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Section: Discussionsupporting

confidence: 92%

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

Soto-Valero¹

2017

Rev. int. cienc. deporte

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“…Under these conditions, PB deteriorates due to both local and general fatigue, increasing the risk for traumatic musculoskeletal injuries 26. Following fatigue, the reduced afferent inflow from the sensory system results in a deterioration of proprioceptive feedback, as well as a reduction in the ability of the muscles to correct the stance and help to maintain correct posture 27.…”

Section: Discussionmentioning

confidence: 99%

A balance and proprioception intervention programme to enhance combat performance in military personnel

Funk

Jacob²,

D³

et al. 2017

J R Army Med Corps

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The proprioceptive intervention programme for combat soldiers improved postural balance on unstable surfaces, and improved the correlation between postural balance in the eyes closed condition and postural balance following fatigue. Further longitudinal studies are needed to verify the relationship between proprioception programmes, additional weight bearing and the reduction of subsequent injuries in combat soldiers.

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“…During soccer matches the running speed over short distances is a determinant factor of the players' performance, which is also influenced by other physiological and psychological factors (Dellal & Wong, 2013;Köklü, Alemdaroğlu, Özkan, Koz, & Ersöz, 2015;Pau, Ibba, Leban, & Scorcu, 2014). According to Kawamori, Hosaka, & Newton (2013) faster players have an advantage during the match and specifically during the critical moments due to they can get the ball in a counter-attack or recovering the ball in open spaces before than the opponent players.…”

Section: Introductionmentioning

confidence: 99%

Who runs the fastest? Anthropometric and physiological correlates of 20 m sprint performance in male soccer players

Nikolaidis¹,

Ruano

Oliveira

et al. 2016

Research in Sports Medicine

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The aim of the present study was to examine the relationship of 20 m sprint performance with anthropometrical and physiological parameters in male soccer players. A hundred and 81 soccer players from the region of Athens (age 23.4 ± 5.0 yrs, body mass 73.4 ± 7.7 kg, height 180.0 ± 5.9 cm, body fat (BF) 14.4 ± 3.6%), classified into quartiles according to 20 m sprint time (group A, 2.84-3.03 s; group B, 3.04-3.09 s; group C, 3.10-3.18 s; group D, 3.19-3.61 s), participated. Soccer players in group A were younger and had better performance in vertical jumps and in the Wingate anaerobic test (WAnT, p < 0.05). Sprint time correlated to age (r = 0.27), body mass (r = 0.

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Characterization of Static Balance Abilities in Elite Soccer Players by Playing Position and Age

Cited by 38 publications

References 25 publications

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

A Gaussian mixture clustering model for characterizing football players using the EA Sports' FIFA video game system. [Modelo basado en agrupamiento de mixturas Gaussianas para caracterizar futbolistas utilizando el sistema de videojuegos FIFA de EA Sports].

A balance and proprioception intervention programme to enhance combat performance in military personnel

Who runs the fastest? Anthropometric and physiological correlates of 20 m sprint performance in male soccer players

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