Comparison of individual playing styles in football

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
Tianyu Guan, Sumit Sarkar, Tim B. Swartz
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引用次数: 0

Abstract

This paper attempts to identify football players who have a similar style to a player of interest. Playing style is not adequately quantified with traditional statistics, and therefore style statistics are created using tracking data. Tracking data allow us to monitor players throughout a match, and therefore include both “on-the-ball” and “off-the-ball” observations. Having developed style features, tractable discrepancy measures are introduced that are based on Kullback–Leibler divergence in the context of multivariate normal distributions. Examples are provided where a pool of players from the Chinese Super League are identified as having a playing style that is similar to players of interest.
足球运动中个人比赛风格的比较
本文试图找出与相关球员风格相似的足球运动员。传统的统计方法无法充分量化球员的踢球风格,因此我们使用跟踪数据来创建风格统计。跟踪数据允许我们在整场比赛中对球员进行监控,因此包括 "球内 "和 "球外 "观察。在开发了风格特征后,我们引入了基于多变量正态分布中库尔贝-莱布勒发散的可操作性差异度量。举例说明了从中国足球超级联赛球员库中识别出与相关球员具有相似比赛风格的球员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Quantitative Analysis in Sports
Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
2.00
自引率
12.50%
发文量
15
期刊介绍: The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.
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