Analyzing Soccer Players’ Skill Ratings Over Time Using Tensor-Based Methods

Abstract

Soccer players have a variety of skills such as passing, tackling, shooting and dribbling. However, their abilities are not fixed and evolve over time. Understanding this evolution could be interesting from many perspectives. We analyze player skill data from the FIFA video game series by EA Sports using tensor methods. This data can be organized as a tensor over three dimensions, namely players, skills, and age, which we explore in two different ways. First, we use a polyadic decomposition to uncover hidden structures among skills and see how these structures evolve over time. Second, we use a Tucker decomposition to predict how a specific player’s skills will evolve over time.