Football data analytics finding patterns in passing behaviours

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football passes

Passing is the backbone of football and forms the basis of important decisions made by managers and owners, such as buying players, picking offensive or defensive strategies or even defining a style of play. These decisions can be supported by analysing how a player performs and how his style affects team performance. The flow of a player or a team can be studied by finding unique patterns in passing (motifs) from the patterns in the subgraphs of a possession-passing network of football games.

These flow motifs can be used to analyse individual players and teams based on the diversity and frequency of their involvement in different motifs. This gives us information about the “style” of a player/team. This style is represented in a unique fashion, as shown in the figure, and can be used to compare players, teams and seasons. Building on theflow motif analysis, an expected goals model can measure the effectiveness of each style of play.

Football data analytics

Our data set has the last 4 seasons of 6 big European leagues with 8219 matches, 3532 unique players and 155 unique teams. Flow motifs can be used to analyse different events, for example the transfer of Claudio Bravo to Pep Guardiola’s Manchester City, why is Jean Seri an elite mifielder, and the difference in attacking style between Lionel Messi and Cristiano Ronaldo. The paper also analyses Post-Fàbregas Arsenal wherein different techniques are combined to analyse the impact the acquisition of Mesut Özil and Alexis Sánchez had on the strategies implemented at Arsenal.

Article by Shaunak Dabadghao, Tu/e

► This paper “Flow Motifs in Soccer: What can passing behavior tell us?“, written by Shaunak Dabadghao,  was a finalist in the Research Paper Competition at the MIT Sloan Sports Analytics Conference (SSAC). The MIT SSAC is the leading conference for industry professionals, researchers and students to discuss the increasing role of analytics in the global sports industry.

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