Analysis on the construction of sports match prediction model using neural network

Abstract

To grasp the development of sports events in time and adjust the strategy in the process of match in time, the traditional back-propagation neural network (BPNN) algorithm was improved, and the match prediction model was constructed by using the adaptive BPNN. Taking the football match data of the Union of European Football Associations Champions League 2016–2017 as the prediction sample, the match was predicted. Moreover, taking some match data of Barcelona in 2016–2017 as an example, the fitting accuracy of the improved adaptive BPNN prediction model, multiple linear regression (MLR) model and grey degree prediction model were compared and analyzed. The research results showed that the prediction model built by the improved adaptive BPNN algorithm had smaller prediction error after rolling prediction. By comparing with the fitting accuracy of MLR model and grey prediction model, it was also found that the prediction error of the prediction model proposed was almost zero, and the error of the other two models was large. It showed that the prediction model proposed had high accuracy and reliability. Therefore, applying neural network to build a sports match prediction model and then predict the match results can provide a certain theoretical basis for sports match practice and the prediction and analysis of the match results.