VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract)
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Sister Conferences Best Papers. Pages 4696-4700. https://doi.org/10.24963/ijcai.2020/648
Despite the fact that objectively assessing the impact of the individual actions performed by soccer players during games is a crucial task, most traditional metrics have substantial shortcomings. First, many metrics only consider rare actions like shots and goals which account for less than 2% of all on-the-ball actions. Second, they fail to account for the context in which the actions occurred. This work summarizes several important contributions. First, we describe a language for representing individual player actions on the pitch. This language unifies several existing formats which greatly simplifies automated analysis and this language is becoming widely used in the soccer analytics community. Second, we describe our framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. This framework enables giving a broad overview of a player's performance, including quantifying a player's total offensive and defensive contributions to their team. Third, we provide illustrative use cases that highlight the working and benefits of our framework.
Machine Learning Applications: Applications of Supervised Learning
Machine Learning: Time-series;Data Streams
Data Mining: Classification, Semi-Supervised Learning
Multidisciplinary Topics and Applications: Other