Clustering of Trajectory Data obtained from Soccer Game Records – A First Step to Behavioral Modeling –

  • Shoji Hirano
  • Shusaku Tsumoto


Ball movement in a soccer game can be measured as a trajectory on two dimentional plane, which summarizes the tactic or strategy of game players. This paper gives a first step to extract knowledge about strategy of soccer game by using clustering of trajectory data, which consists of the following two steps. First, we apply a pairwise comparison of two trajectories using multiscale matching. Next, we apply rough-set based clustering technique to the similarity matrix obtained by the pairwise comparisons. Experimental results demonstrated that the method could discover some interesting pass patterns that may be associated with successful goals.


Planar Curf Trajectory Data Pass Action Soccer Game Pass Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F. Mokhtarian and A. K. Mackworth (1986): Scale-based Description and Recognition of planar Curves and Two Dimensional Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1): 24-43CrossRefGoogle Scholar
  2. 2.
    N. Ueda and S. Suzuki (1990): A Matching Algorithm of Deformed Planar Curves Using Multiscale Convex/Concave Structures. IEICE Transactions on Information and Systems, J73-D-II(7): 992–1000.Google Scholar
  3. 3.
    S. Hirano and S. Tsumoto (2003): An Indiscernibility-Based ClusteringMethod with Iterative Refinement of Equivalence Relations - Rough Clustering -. Journal of Advanced Computational Intelligence and Intelligent Informatics, 7(2):169–177.Google Scholar
  4. 4.
    A. Yamada, Y. Shirai, and J. Miura (2002): Tracking Players and a Ball in Video Image Sequence and Estimating Camera Parameters for 3D Interpretation of Soccer Games. Proceedings of the 16th International Conference on Pattern Recognition (ICPR-2002), 1:303–306.Google Scholar
  5. 5.
    Y. Gong, L. T. Sin, C. H. Chuan, H. Zhang, and M. Sakauchi (1995): Automatic Parsing of TV Soccer Programs. Proceedings of the International Conference onMultimedia Computing and Systems (ICMCS’95), 167–174.Google Scholar
  6. 6.
    T. Taki and J. Hasegawa (2000): Visualization of Dominant Region in Team Games and Its Application to Teamwork Analysis. Computer Graphics International (CGI’00), 227–238.Google Scholar
  7. 7.
    T. Lindeberg (1990): Scale-Space for Discrete Signals. IEEE Trans. PAMI, 12(3), 234–254.Google Scholar
  8. 8.
    B. S. Everitt, S. Landau, and M. Leese (2001): Cluster Analysis Fourth Edition. Arnold Publishers.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Shoji Hirano
    • 1
  • Shusaku Tsumoto
    • 1
  1. 1.Shimane UniversityIzumoJapan

Personalised recommendations