Comparison of Machine Learning Methods for Predicting the Recovery Time of Professional Football Players After an Undiagnosed Injury

Injuries are a common problem in professional football. A challenge that the medical team faces is to successfully predict the recovery time of an injured player. Current medical standards can only give vague predictions as to when a player will return to play. Obviously, making an accurate prediction as soon as possible would be helpful to the coach. This research tries to answer the question of whether it is possible to predict when a player will return to play, based on information at the moment of injury, while also comparing three machine learning methods for this task: support vector machines, Gaussian processes and neural networks. The tests were conducted on data from the professional football club of Tottenham Hotspur. The results demonstrate that this task can be completed with a reasonable amount of accuracy, without any method performing significantly better than the rest. Future directions and possible improvements are discussed.