Journal of Systems Science and Complexity

, Volume 26, Issue 1, pp 43–61 | Cite as

Extracting behavioural models from 2010 FIFA world cup

  • Héctor Menéndez
  • Gema Bello-Orgaz
  • David Camacho
Article

Abstract

The FIFA World Cup™ is the most profitable worldwide event. The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition. This work is focused on the extraction of behavioural patterns for both, players and teams strategies, through the automated analysis of this dataset. The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting. However, the main contribution is related to the study on several automatic knowledge extraction techniques, such as clustering methods, and how these techniques can be used to obtain useful behavioural models from a global statistics dataset. The information provided by the clustering algorithms shows similar properties which have been combined to define the models, making the human interpretation of these statistics easier. Finally, the most successful teams strategies have been analysed and compared.

Key words

Behavioural patterns clustering FIFA World Cup football soccer web mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Dobson S and Goddard J A, The Economics of Football, Cambridge University Press, Cambridge, 2011.CrossRefGoogle Scholar
  2. [2]
    Aler R, Valls J M, Camacho D, and Lopez A, Programming robosoccer agents by modeling human behavior, Expert Systems with Applications, 2009, 36: 1850–1859.CrossRefGoogle Scholar
  3. [3]
    Grollman D H and Jenkins O C, Learning robot soccer skills from demonstration, International Conference on Development and Learning, 2007, 276–281.Google Scholar
  4. [4]
    Jiménez-Díaz G, Menéndez H D, Camacho D, and González-Calero P A, Predicting performance in team games, INSTICC Institude for systems, Control Technologies of Information, and Communication, editors, ICAART 2011 — Proceedings of the 3 rd International Conference on Agents and Artificial Intelligence, 2011.Google Scholar
  5. [5]
    Leng J S, Fyfe C, and Jain L, Reinforcement learning of competitive skills with soccer agents, Knowledge-Based Intelligent Information and Engineering Systems, Springer, 2010, LNCS 4692: 572–579.Google Scholar
  6. [6]
    Vaz de Melo P O S, Almeida V A F, and Loureiro A A F, Can complex network metrics predict the behavior of nba teams? Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, ACM, KDD’ 08, 2008.Google Scholar
  7. [7]
    Onody R N and De Castro P A, Complex network study of Brazilian soccer players, Phys. Rev. E, 2004, 70: 037103.CrossRefGoogle Scholar
  8. [8]
    Bittner E, NuBbaumer A, Janke W, and Weigel M, Self-affirmation model for football goal distributions, EPL (Europhysics Letters), 2007, 78(5): 58002.CrossRefGoogle Scholar
  9. [9]
    Cotta C, Mora A M, Merelo-Molina C, and Guervós J J M, Fifa world cup 2010: A network analysis of the champion team play, CoRR, abs/1108.0261, 2011.Google Scholar
  10. [10]
    Larose D T, Discovering Knowledge in Data, John Wiley & Sons, New Jersey, 2005.MATHGoogle Scholar
  11. [11]
    Ng A, Jordan M, and Weiss Y, On Spectral Clustering: Analysis and an algorithm (ed. by Dietterich T, Becker S, and Ghahramani Z), Advances in Neural Information Processing Systems, MIT Press, 2001, 849–856.Google Scholar
  12. [12]
    Kohavi R and John G H, Wrappers for feature subset selection, Artif. Intell., 1997, 97: 273–324.MATHCrossRefGoogle Scholar
  13. [13]
  14. [14]
    Delac K, Grgic M, and Grgic S, Independent comparative study of PCA, ICA, and LDA on the FERET data set, International Journal of Imaging Systems and Technology, 2005, 15(5): 252–260.CrossRefGoogle Scholar
  15. [15]
    Carroll S R and Carroll D J, Statistics Made Simple for School Leaders, Rowman & Littlefield, 2002.Google Scholar
  16. [16]
    Han J W and Kamber M, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006.Google Scholar
  17. [17]
    MacKay D, Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge, 2003.MATHGoogle Scholar

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Héctor Menéndez
    • 1
  • Gema Bello-Orgaz
    • 1
  • David Camacho
    • 1
  1. 1.Department of Computer ScienceUniversidad Autónoma de MadridMadridSpain

Personalised recommendations