Extracting behavioural models from 2010 FIFA world cup
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 wordsBehavioural patterns clustering FIFA World Cup football soccer web mining
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- Grollman D H and Jenkins O C, Learning robot soccer skills from demonstration, International Conference on Development and Learning, 2007, 276–281.Google Scholar
- 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
- 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
- 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
- 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
- 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
- Fifa web site, 2011. http://www.fifa.com/worldcup/archive/southafrica2010/statistics/index.html.
- Carroll S R and Carroll D J, Statistics Made Simple for School Leaders, Rowman & Littlefield, 2002.Google Scholar
- Han J W and Kamber M, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006.Google Scholar