Laszlo Gyarmati, Senior Software Engineer, Qatar Computing Research Institute
Mohamed Hefeeda, Principal Scientist, Qatar Computing Research Institute
Abstract: It is challenging to get access to datasets related to the physical performance of soccer players. The teams consider such information highly confidential, especially if it covers in-game performance. Despite the fact that numerous teams deployed player tracking systems in their stadiums, datasets of this nature are not available for research or for public usage. Hence, most of the analysis and evaluation of the players’ performance do not contain much information on the physical aspect of the game, creating a blindspot in performance analysis. We propose a novel method to solve this issue by deriving movement characteristics of soccer players. We use event-based datasets from data provider companies covering 50+ soccer leagues allowing us to analyze the movement profiles of potentially tens of thousands of players without any major investment. Our methodology does not require expensive, dedicated player tracking system deployed in the stadium. Instead, if the game is broadcasted, our methodology can be used. We also compute the similarity of the players based on their movement characteristics and as such identify potential candidates who may be able to replace a given player. Finally, we quantify the uniqueness and consistency of players in terms of their in-game movements. Our study is the first of its kind that focuses on the movements of soccer players at scale, while it derives novel, actionable insights for the soccer industry from event-based datasets.