Analysis of association football playing styles: an innovative method to cluster networks

TL;DR

This paper introduces a hierarchical clustering method to analyze and categorize football team playing styles based on network representations, revealing key tactics and their impact on game outcomes.

Contribution

The paper presents a novel hierarchical clustering approach that incorporates population characteristics to classify football team styles from network data.

Findings

Identified 15 main tactics in Serie A 2015-2016 season.

Demonstrated the influence of playing styles on goal scoring.

Extended Dixon and Coles model to assess style effects.

Abstract

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 prove the key role of one of them by implementing an extension of the Dixon and Coles model (Dixon and Coles, 1997).