A deep learning framework for football match prediction

Abstract

An efficient framework is developed by deep neural networks (DNNs) and artificial neural network (ANNs) for predicting the outcomes of football matches. A dataset is used with the rankings, team performances, all previous international football match results and so on. ANN and DNN are used to explore and process the sporting data to generate prediction value. Datasets are divided into sections for training, validating and testing. By using the proposed DNN architecture, corresponding model performed excellently on predicting the FIFA world cup 2018 matches. This model had predicted 63.3% matches accurately. However, this accuracy can be increased with proper datasets and more accurate information of the teams. The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events. For more accurate performance of the prediction, prior and more information about each team, player and match is desirable.