Analysis of association football playing styles: an innovative method to cluster networks
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In this work we develop an innovative hierarchical clustering method to divide a sample of undirected weighted networks into groups. The methodology consists of two phases: the first phase is aimed at putting the single networks in a broader framework by including the characteristics of the population in the data, while the second phase creates a subdivision of the sample on the basis of the similarity between the community structures of the processed networks. Starting from the representation of the team's playing style as a network, we apply the method to group the Italian Serie A teams' performances and consequently detect the main 15 tactics shown during the 2015-2016 season. The information obtained is used to verify the effect of the styles of play on the number of goals scored, and we demonstrate the key role of one of them by implementing an extension of the Dixon and Coles model (Dixon and Coles, 1997). In addition, a simulation study is provided to test the reliability of our approach in widely differing scenarios.
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