Degree Distribution of Position-Dependent Ball-Passing Networks in Football Games

Narizuka, Takuma; Yamamoto, Ken; Yamazaki, Yoshihiro

doi:10.7566/jpsj.84.084003

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Narizuka et al found that the networks had the small-world property, and their degree distributions were well fitted by a truncated gamma distribution [35]. They also proposed a simple stochastic model describing the spatial-dependent passing network [36]. Mclean et al used four equalsized zones to describe the spatial information of footballers.…”

Section: Spatial-dependent Passing Networkmentioning

confidence: 99%

A survey on football network analysis

Li,

Xu,

Zhou

2023

EPL

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Being the world's most popular sport, football research has traditionally concentrated on empirical summaries or statistics, with only limited data available in the past. In recent years, social network analysis has been applied to a variety of fields, which also brings new perspectives to the study of football spots. In this paper, we survey the literature related to football networks and discuss the use of network measures to analyze the performance of footballers and teams in different types of football networks. We aim to find out how to construct appropriate football networks based on different perspectives on football research. Various studies on football network analysis, including team performance, player interactions, and club behavior, are reviewed. The findings provide insights into team performance, player roles, and social dynamics within football teams and clubs.

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Section: Spatial-dependent Passing Networkmentioning

confidence: 99%

A survey on football network analysis

Li,

Xu,

Zhou

2023

EPL

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“…Meanwhile, ball motion on the field is essential in football. Actually, complex network analysis of the ball-passing [16][17][18][19], fractal analysis of the ball motion [20], and statistical analysis of the ball-possession time [21] have been done.…”

Section: Introductionmentioning

confidence: 99%

Statistical properties for directional alignment and chasing of players in football games

Narizuka

Yamazaki

2016

EPL

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Focusing on motion of two interacting players in football games, two velocity vectors for the pair of one player and the nearest opponent player exhibit strong alignment. Especially, we find that there exists a characteristic interpersonal distance r 500 cm below which the circular variance for their alignment decreases rapidly. By introducing the order parameter φ(t) in order to measure degree of alignment of players' velocity vectors, we also find that the angle distribution between the nearest players' velocity vectors becomes wrapped Cauchy (φ 0.7) and the mixture of von Mises and wrapped Cauchy distributions (φ 0.7), respectively. To understand these findings, we construct a simple model for the motion of the two interacting players with the following rules: chasing between the players and the reset of the chasing. We numerically show that our model successfully reproduce the results obtained from the actual data. Moreover, from the numerical study, we find that there is another characteristic distance r 1000 cm below which player's chasing starts.

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