The use of tracking data in the field of sport analytics has increased in the last years as a starting point for detailed analyses about several aspects of a match. In parallel, Temporal Convolutional Networks have established themselves as a powerful way to process sequential data: TCNs have outperformed RNNs on different tasks and are based on spatiotemporal convolutions, enabling to exploit the improvements in parallelization technologies. This work uses TCNs to classify the status of a football match, which can take on one of three possible values: inactive game, ball owned by the home team, ball owned by the away team.