Measuring soccer players’ contributions to chance creation by valuing their passes

Author:

Bransen Lotte1,Van Haaren Jan1,van de Velden Michel2

Affiliation:

1. SciSports , Amersfoort , The Netherlands

2. Erasmus Universiteit Rotterdam , Rotterdam , The Netherlands

Abstract

Abstract Scouting departments at soccer clubs aim to discover players having a positive influence on the outcomes of matches. Since passes are the most frequently occurring on-the-ball actions on the pitch, a natural way to achieve this objective is by identifying players who are effective in setting up chances. Unfortunately, traditional statistics such as number of assists fail to reveal players excelling in this area. To overcome this limitation, this paper introduces a novel metric that measures the players’ involvement in setting up chances by valuing the effectiveness of their passes. Our proposed metric identifies Arsenal player Mesut Özil as the most impactful player in terms of passes during the 2017/2018 season and proposes Ajax player Frenkie de Jong as a suitable replacement for Andrés Iniesta at FC Barcelona.

Publisher

Walter de Gruyter GmbH

Subject

Decision Sciences (miscellaneous),Social Sciences (miscellaneous)

Reference27 articles.

1. Barnard, M., M. Dwyer, J. Wilson, and C. Winn. 2018. “Annual Review of Football Finance 2018.” https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/sports-business-group/deloitte-uk-sbg-annual-review-of-football-finance-2018.PDF.

2. Beetz, M., N. von Hoyningen-Huene, B. Kirchlechner, S. Gedikli, F. Siles, M. Durus, and M. Lames. 2009. “ASPOGAMO: Automated Sports Game Analysis Models.” International Journal of Computer Science in Sport 8(1):1–21.

3. Brochu, E., V. M. Cora, and N. De Freitas. 2010. “A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning.” arXiv preprints 1012.2599.

4. Cervone, D., A. D’Amour, L. Bornn, and K. Goldsberry. 2016. “A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes.” Journal of the American Statistical Association 111(514):585–599.

5. Chawla, S., J. Estephan, J. Gudmundsson, and M. Horton. 2017. “Classification of Passes in Football Matches Using Spatiotemporal Data.” ACM Transactions on Spatial Algorithms and Systems (TSAS) 3(2):6.

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3