Prediction Model of the Impact of Refereeing Decisions Based on BP Neural Network Algorithm on Football Match Results
This study constructs a prediction model based on BP neural network algorithm using the refereeing data from the last three provincial sports meets in football matches,analyzing the correlation effect of refereeing decisions on match results.By preprocessing the data and extracting features for input into the neural network for training,an effective prediction model is established.The results indicate that:(ⅰ)when the number of hidden layer nodes in the BP network is set to 6,the relative mean square error of the training set predictions is relatively small,achieving an accuracy rate of over 90％in predicting future match results,with evaluation metrics such as recall rate and F1 score performing well.(ⅱ)The refereeing situation is significantly positively correlated with football match results,serving as an important factor influencing match results.This study provides a new perspective for predicting the impact of refereeing decisions on football match results and lays a theoretical foundation for analyzing and predicting the effects of refereeing in other sports competitions.
BP neural networkrefereeing decisionsfootball match resultsprediction model
NETL
NSTL
万方数据
