A new model to forecast the results of matches based on hybrid neural networks in the soccer rating system

The objective of this paper is to build a result prediction model for the rating system in soccer games. A rating system which plays a crucial role in world sports field yields predictions for the probability that one contestant beats another. The result prediction model is the core technique in the rating system. The robustness and accuracy of the model is a very important feature because people will trust the rating system only if it can give the exact prediction of the game results. This paper employs a coarse-to-fine training technique based on hybrid neural network. Very few people have ever attempted the method based on neural network before in this field. First a match is classified into three categories with a LVQ net to determine the strength contrast between two contestants. Then the elaborately designed data will go through the specific BP nets according to the classifying result. The model is trained and tested on volumes of actual soccer match results from Italian series A. Finally the results of the model are compared to other prediction models based on statistics. The outcome shows that the new model is more accurate and provides better performance evaluation of all teams.