Using Diagnostic Analysis to Discover Offensive Patterns in a Football Game

Tianbiao Liu (), Philippe Fournier-Viger and Andreas Hohmann
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Tianbiao Liu: College of Sports and PE, Beijing Normal University
Philippe Fournier-Viger: School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen)
Andreas Hohmann: Institute of Sports Science, University of Bayreuth

Chapter Chapter 43 in Recent Developments in Data Science and Business Analytics, 2018, pp 381-386 from Springer

Abstract: Abstract Football is a popular team sport, for which analyzing a team strategies can reveal useful information for understanding and improving a team’s performance. For this purpose, a promising approach is to analyze data collected about a match using data mining algorithms. However, designing such approach is not trivial as a football match involves both the time dimension and the spatial dimension. In this paper, a diagnostic analysis based approach is proposed, which consists of preparing data from a match by considering the spatial dimension and then extracting sequential rules from the data. The proposed approach is illustrated in a case study to analyze the match between Germany and Italy at the 2012 European Championship. Results of this study show that threatening offensive patterns from the Germany team are identified, illustrating complex interactions between players for performance analysis.

Keywords: Football; Performance analysis data mining; Sequential rules; European Championship (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-72745-5_43

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