Keynote Speaker: Prof. Jesse Davis, Lecturer, Computer Sciences, KU Leuven, Belgium

Currently, it is possible to collect massive amounts of data about sports matches and athletes. In particular, there are now extremely large datasets detailing what occurs during matches. Consequently, there has been an explosion of interest in using AI techniques to analyze this data. In the context of professional soccer, one of the key use cases is in player recruitment. Here, the key role that data can play is in helping to quantify the impact of the individual actions performed by soccer players during games. A variety of different metrics have been proposed for this problem. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. In this talk, I will describe a data-driven framework for valuing on-the-ball actions in a soccer game. A key benefit of our approach is that it considers all types of actions (e.g., passes, crosses, dribbles, take-ons, and shots) and accounts for the circumstances under which each of these actions happened as well as their possible longer-term effect. I will present several use cases that illustrate the insights our models provide, and how they can benefit practitioners. And, we will touch on the ongoing debate- GOAT: Messi vs Ronaldo