Research On The Application Of BP Neural Network In Predicting The Outcome Of Football Matches

Football is the first sport in the world,and a billion people around the world watch football.As the popularity of football matches increased,so too did the predictions of the outcome of the game.In today's information to develop,traditional prediction methods have been unable to meet the needs of football matches the outcome prediction,and to deal with nonlinear problem ability of the BP neural network as a representative of the new forecasting method is more popular with the experts and scholars.This paper takes the Chinese super league competitions as the research object,through the establishment of the BP neural network model,using historical data to predict the football competition,and completed the forecast system of China football super league competition prototype development.The specific research content contains several aspects:Firstly,this paper describes the application background of football game forecast,the research and application of BP neural network algorithm,and the relevant research on the prediction of football match;Secondly,this paper analyzes the factors that influence the outcome of football match,and summarizes the applicability of BP neural network algorithm in forecasting applications in football games;Thirdly,this paper designed a suitable for football competition BP neural network model to predict the three layers of structure,using Java web scraping techniques to obtain the Chinese super league tournament history data for the algorithm testing;Fourthly,the design of the overall design of the system and the design of the database was completed through the analysis of system requirements;Fifthly,this paper has developed the prototype of the winning and winning prediction system based on the Java language.Compared with traditional forecast methods,using the BP neural network prediction accuracy is greatly improved,embodies the BP neural network prediction in the football competition in the field of practicality.By using existing data,the average accuracy is 66.5%.