The collection, analysis and exploitation of footballer attributes: A systematic review

Pub Date : 2022-02-08 DOI:10.3233/jsa-200554
Edward Wakelam, V. Steuber, James Wakelam
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Abstract

There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.
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足球运动员特质的收集、分析与利用:系统综述
越来越多的研究正在进行中,研究在比赛前、比赛中和比赛后收集的足球运动员属性如何满足俱乐部、媒体专家和游戏开发商的需求。专注于个人球员的表现分析和预测,我们检查了考虑不同球员属性的研究主体。这使得1999年至2020年间发表的132篇相关论文被选中。根据这些,我们编制了一份全面的球员属性列表,将其分为静态(如年龄和身高)或动态(如传球完成和射门命中)。为了表明它们的准确性,我们将每个属性分类为客观或主观衍生的,最后根据它们隐含的可及性以及它们可能的个人和俱乐部敏感性。我们将这些属性分为25个逻辑组,如传球、铲球和球员人口统计。我们分析了每组的相对研究重点,并注意到所使用的分析方法,确定使用了哪些统计或机器学习技术。我们回顾并考虑了所选论文中性格特征属性的使用,并讨论了更正式的使用方法。在此基础上,我们就如何开展这项工作以支持精英俱乐部考虑转会目标提出了建议。
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