research-article

Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data

Authors:

Jan Van Haaren,

Jesse DavisAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 223 - 232

https://doi.org/10.1145/3219819.3219832

Published: 19 July 2018 Publication History

Abstract

Sports teams are nowadays collecting huge amounts of data from training sessions and matches. The teams are becoming increasingly interested in exploiting these data to gain a competitive advantage over their competitors. One of the most prevalent types of new data is event stream data from matches. These data enable more advanced descriptive analysis as well as the potential to investigate an opponent's tactics in greater depth. Due to the complexity of both the data and game strategy, most tactical analyses are currently performed by humans reviewing video and scouting matches in person. As a result, this is a time-consuming and tedious process. This paper explores the problem of automatic tactics detection from event-stream data collected from professional soccer matches. We highlight several important challenges that these data and this problem setting pose. We describe a data-driven approach for identifying patterns of movement that account for both spatial and temporal information which represent potential offensive tactics. We evaluate our approach on the 2015/2016 season of the English Premier League and are able to identify interesting strategies per team related to goal kicks, corners and set pieces.

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Index Terms

Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
      2. Data stream mining

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cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 July 2018

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Skoki AIvić SLjubic SLerga JŠtajduhar I(2024)Enhancing Biophysical Muscle Fatigue Model in the Dynamic Context of SoccerSensors10.3390/s2424812824:24(8128)Online publication date: 19-Dec-2024
https://doi.org/10.3390/s24248128
Muñoz OMonroy RCañete-Sifuentes LRamirez-Marquez J(2024)Automated Discovery of Successful Strategies in Association FootballApplied Sciences10.3390/app1404140314:4(1403)Online publication date: 8-Feb-2024
https://doi.org/10.3390/app14041403
Zhang QWang ZLong CYiu S(2024)Billiards Sports Analytics: Datasets and TasksACM Transactions on Knowledge Discovery from Data10.1145/368680418:9(1-27)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3686804
Cao AXie XZhang RTian YFan MZhang HWu Y(2024)Team-Scouter: Simulative Visual Analytics of Soccer Player ScoutingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345621631:1(1-11)Online publication date: 10-Sep-2024
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Pu ZPan YWang SLiu BChen MMa HCui Y(2024)Orientation and Decision-Making for Soccer Based on Sports Analytics and AI: A Systematic ReviewIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2023.12380711:1(37-57)Online publication date: Jan-2024
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