Leveraging Player Embedding for Soccer Event Prediction

Abstract

Soccer is one of the least constrained and most complex team sports, which makes it extremely hard to capture its behavior. In recent years, attempts have been made, mainly using machine learning techniques, to predict event sequences that indicate which player has taken what action where in a soccer match. However, the prediction of an event seems to depend not only on the previous event sequences, but also to a large extent on which player performs the action. In this study, we propose leveraging a distributed representation, i.e. a vector representation of the players as input for a neural event prediction model. In this way, the model can take into account player characteristics that have not been considered in previous studies. Our results demonstrated that "player vectors" improved the performance of neural event prediction models and that these vectors contain relevant information about player roles.