Predicting Goal-Scoring Opportunities in Soccer by Using Deep Convolutional Neural Networks

Over the past decades, soccer has encountered an enormous increase in professionalism. Clubs keep track of their players’ mental condition, physical condition, performances on the pitch, and so on. New technology allows for the automation of some of these processes. For instance, position data captured from soccer training and matches can be used to keep track of player fitness. There are opportunities for the use of position data in artificial intelligence as well. In the current research, position data gathered from matches played by a German Bundesliga team has been used to predict goal-scoring opportunities. The problem was approached as one of classification: given a snapshot of position data, is it more likely that a goal-scoring opportunity will be created or that ball possession will be lost? Snapshots of position data were taken and transformed to 256× 256 images, which were used as input for machine learning algorithms. The performance of two deep convolutional neural networks was compared: an instance of GoogLeNet and a less complex 3-layered net. GoogLeNet came out as the best performing network with an average accuracy of 67%. Although the final performance was not spectacular, there are some promising indicators for future research and possible practical uses.