Characterizing Soccer Players' Playing Style from Match Event Streams

Transfer fees for soccer players are at an all-time high. To make the most of their budget, soccer clubs need to understand the type of players they have and the type of players that are on the market. Current insights in the playing style of players are mostly based on the opinions of human soccer experts such as trainers and scouts. Unfortunately, their opinions are inherently subjective and thus prone to faults. In this paper, we characterize the playing style of a player in a more rigorous, objective and data-driven manner. We capture the playing style of a player in a so-called 'player vector' that can be interpreted both by human experts and machine learning systems. We demonstrate the validity of our approach by recovering commonly known player types (e.g., left-winger, right-center defender) through unsupervised clustering and by substantiating a number of claims in popular media about soccer players (e.g., "Paolo Dybala is the new Lionel Messi") with our results.

Publication year:2019

Accessibility:Open