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The scientific challenge is about unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of game play of both countries,, combining expertise from data science, computer science and sport science. Suggested features from literature, as well as several novel ones, will be considered and filtered on how they capture success in soccer. A manageable set of features will then be obtained from various available Dutch datasets (focusing on successful play). Subsequently, the same features will be used to compare playing styles between both countries.
Features of game play will be approached from two different angles. The first angle (spearheaded by the Brazilian computer science partner) concerns features that capture the dynamics of game play and characterize aspects of formation on the pitch. The second angle (lead by the Dutch data science partner) will focus on how an attack is built up, and how key events (shots on goal, transitions from defenders to midfielders, etc.) can help to characterize this.
For the comparison between countries data will be collected in four different age categories in Brazil and the Netherlands during official games, in order to compare (the development of) game play between both countries. Data will be collected by means of the Local Position Measurement System, for reasons of accuracy and consistency.
The applied science part of this proposal is focusing on bridging the gap between fundamental science and soccer practice, i.e. coaches, trainers, clubs and federations. The outcomes of the fundamental part will be implemented in a coach-cockpit, a software application which trainers and coaches can use to (1) decide upon their strategy before a game, (2) analyze player- and team behaviour during a game enabling to adjust the strategy accordingly, and (3) choose and/or design training forms to improve player- and team behaviour.