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Using Diagnostic Analysis to Discover Offensive Patterns in a Football Game

  • Tianbiao Liu
  • Philippe Fournier-Viger
  • Andreas Hohmann
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

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 

Notes

Acknowledgements

The authors would like to thank all the participants involved in this work. Markus Kraus and Simon Scholz are acknowledged for their important data collection and comments. The Project is sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry and also supported by “the Fundamental Research Funds for the Central Universities (Youth Scholars Program of Beijing Normal University)”.

References

  1. 1.
    Fournier-Viger, P., Wu, C.-W., Tseng, V. S., Cao, L., & Nkambou, R. (2015). Mining partially-ordered sequential rules common to multiple sequences. IEEE Transactions on Knowledge and Data Engineering, 27(8), 2203–2216.CrossRefGoogle Scholar
  2. 2.
    Greve, W., & Wentura, D. (1997). Wissenschaftliche Beobachtung: Eine Einführung. PVU/Beltz: Weinheim.Google Scholar
  3. 3.
    Hughes, C. F. (1990). The winning formula. London: Collins.Google Scholar
  4. 4.
    Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.CrossRefGoogle Scholar
  5. 5.
    Liu, T., Hohmann, A., Chen, Q., Lei, T., & Xue, J. (2017). Apriori-based performance analysis on offense models of Elite Women’s Football Games: A case study of Algarve Cup 2012. Journal of Shanghai University of Sport, 41(1), 77–82.Google Scholar
  6. 6.
    Liu, T., & Hohmann, A. (2013). Applying data mining to analyze the different styles of offense between Manchester United and FC Barcelona in the European Champions League Final. International Journal of Sports Science and Engineering, 7(02), 067–078.Google Scholar
  7. 7.
    Liu, T. (2014). Systematische spielbeobachtung im internationalen leistungsfußball.Google Scholar
  8. 8.
    Mabroukeh, N. R., & Ezeife, C. I. (2010). A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(1), 1–41.CrossRefGoogle Scholar
  9. 9.
    Xue, J., Li, Y., & Guo, C. (2007). Study on general pattern of attacking for goal character of each team in the final in 18th World Soccer Cup. China Sport Science and Technology, 43(1), 36–40.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Tianbiao Liu
    • 1
  • Philippe Fournier-Viger
    • 2
  • Andreas Hohmann
    • 3
  1. 1.College of Sports and PE, Beijing Normal UniversityBeijingChina
  2. 2.School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen)ShenzhenChina
  3. 3.Institute of Sports Science, University of BayreuthBayreuthGermany

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