摘要
The FIFA World Cup^(TM) 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.
The FIFA World Cup^TM 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.
基金
partly supported by:Spanish Ministry of Science and Education under project TIN201019872
the grant BES-2011-049875 from the same Ministry
Jobssy.com company under project FUAM076913
