A neural network method for prediction of 2006 World Cup Football Game

Kou-Yuan Huang*, Wen Lung Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

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.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
StatePublished - 1 Dec 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period18/07/1023/07/10

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