Graph Neural Networks for Events Detection in Football

Abstract: Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. In this thesis, we explore the detection of corners, free kicks, and throw-in events by means of neural networks. Training a model to solve this task is not easy; the neural network needs to model the spatio-temporal interactions between different agents moving in a 2-dimensional space. We decided to address this problem using graph neural networks in combination with recurrent neural networks, which allow us to model respectively the spatial and temporal components of the data. Tracking the position of the ball is difficult, which makes the dataset noisy. In this thesis, we mainly work with a version of the dataset where the position of the ball has been manually corrected. However, to study how the noisy position of the ball affects the results we also train the models on the original data. The results show that detecting the corner and the throw-in is much easier than detecting the free kick. Moreover, the noisy position of the ball affects significantly the performance of the model. We conclude that to train the model on the original data it is necessary to use a much larger training set. Since the amount of training data for these events is limited, we also train the model on the more generic ball-dead-to-alive event, for which much more data is available, and we observe that by increasing the amount of training data the results can improve significantly. In this report, we also provide an in-depth discussion about all the challenges faced during the project and how different hyperparameters and design choices can affect the results.