Automated Classification of Passing in Football

  • Michael Horton
  • Joachim Gudmundsson
  • Sanjay Chawla
  • Joël Estephan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9078)


A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. In this paper we consider the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. In addition, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.


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  1. 1.
    de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational geometry. Springer (2008)Google Scholar
  2. 2.
    Borrie, A., Jonsson, G.K., Magnusson, M.S.: Temporal pattern analysis and its applicability in sport: an explanation and exemplar data. Journal of Sports Sciences 20(10), 845–852 (2002)CrossRefGoogle Scholar
  3. 3.
    ChyronHego Corporation, : Tracab player tracking system. (2014)
  4. 4.
    Franks, I.M., Miller, G.: Eyewitness testimony in sport. Journal of Sport Behavior (1986)Google Scholar
  5. 5.
    Gudmundsson, J., Wolle, T.: Towards automated football analysis: Algorithms and data structures. In: Proc. 10th Australasian Conf. on Mathematics and Computers in Sport, Citeseer (2010)Google Scholar
  6. 6.
    Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Computers, Environment and Urban Systems (2013)Google Scholar
  7. 7.
    Horton, M., Gudmundsson, J., Chawla, S., Estephan, J.: Classification of passes in football matches using spatiotemporal data. arXiv preprint arXiv:1407.5093 (2014)
  8. 8.
    Horton, M., Gudmundsson, J., Chawla, S., Estephan, J.: Feature descriptions for pass classifier. (2014)
  9. 9.
    Jenatton, R., Audibert, J.Y., Bach, F.: Structured variable selection with sparsity-inducing norms. The Journal of Machine Learning Research 12, 2777–2824 (2011)MATHMathSciNetGoogle Scholar
  10. 10.
    Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society 7(1), 48–50 (1956)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Leo, M., Mosca, N., Spagnolo, P., Mazzeo, P.L., D’Orazio, T., Distante, A.: Real-time multiview analysis of soccer matches for understanding interactions between ball and players. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, pp. 525–534. ACM, 1386419 (2008)Google Scholar
  12. 12.
    Liu, W., Chawla, S.: A quadratic mean based supervised learning model for managing data skewness. In: SDM, pp. 188–198. SIAM (2011)Google Scholar
  13. 13.
    Nakanishi, R., Maeno, J., Murakami, K., Naruse, T.: An Approximate Computation of the Dominant Region Diagram for the Real-Time Analysis of Group Behaviors. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS, vol. 5949, pp. 228–239. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  14. 14.
    Prozone Sports Ltd: Prozone: Prozone sports - prozone 3 - sports performance analysis. (2013)
  15. 15.
    Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Rusboost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(1), 185–197 (2010)CrossRefGoogle Scholar
  16. 16.
    Taki, T., Hasegawa, J.: Visualization of dominant region in team games and its application to teamwork analysis. In: Proceedings of the Computer Graphics International, pp. 227–235 (2000)Google Scholar
  17. 17.
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc. (1995)Google Scholar
  18. 18.
    Wilson, J.: Inverting the pyramid: The history of football tactics. Hachette UK (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michael Horton
    • 1
  • Joachim Gudmundsson
    • 1
  • Sanjay Chawla
    • 1
    • 2
  • Joël Estephan
    • 1
  1. 1.School of Information TechnologiesThe University of SydneySydneyAustralia
  2. 2.Qatar Computing Research InstituteDohaQatar

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