A Neural Network Method for Prediction of 2006 World Cup Football Game

Abstract

A neural network method is adopted to predict the football game's winning rate of two teams according to their previous stage's official statistical data of 2006 World Cup Football Game. The adopted prediction model is based on multi-layer perceptron (MLP) with back propagation learning rule. The input data are transformed to the relative ratios between two teams of each game. New training samples are added to the training samples at the previous stages. By way of experimental results, the determined neural network architecture for MLP is 8 inputs, 11 hidden nodes, and 1 output (8-11-1). The learning rate and momentum coefficient are sequentially determined by experiments as well. Based on the adopted MLP prediction method, the prediction accuracy can achieve 76.9% if the draw games are excluded.