Assessor Models with a Reject Option for Soccer Result Prediction

Soccer is a hugely popular sport globally and boasts a billion-dollar industry. ML (Machine Learning) algorithms have been adopted to predict outcomes in soccer. However, soccer matches are sometimes difficult to predict even by well informed experts and in other cases have surprising results. In the same sense, ML predictions may be unreliable and inaccurate. In this paper, we investigate a new approach in the ML literature, called assessors, adopted in our work to monitor the quality of predictions of a ML base model along a championship in order to identify the most reliable ones. Given a match, the assessor is used to predict whether the base model is able to correctly predict that match's result. Unreliable predictions are then rejected by the assessor. Our goal is to optimize the accuracy of accepted predictions while controlling the rejection rate. We conducted experiments on real data to identify the championships, teams, and rounds where the proposed rejection option approach performs best. The innovative proposed approach was actually useful to identify high-quality predictions, thus enhancing the reliability of match outcome predictions.