Research on score prediction in sports competitions——based on PSO-BP neural network and ARIMAX hybrid prediction model

Abstract

Predicting match results has always been an important problem in the field of sports. Athletes and coaches can benefit from accurately predicting the path of a match, allowing them to strategize ahead of time and take control during the match. The PSO-BP neural network combines the advantages of particle swarm optimization and backpropagation neural networks to improve the accuracy of the model in processing complex nonlinear data interactions. ARIMAX is ideal for evaluating data that exhibits linear trends and seasonality. The model combines an autoregressive synthetic moving average component with external variables to account for linear trends, seasonality, and external influences in the data. Through the data in https://github.com/JeffSackmann/tennis_wta, this article establishes a combination of two models to predict game scores. Variables include advantage, turnover, lead, distance traveled, and serve. Using K-fold cross-validation, the accuracy rates of the PSO-BP neural network and ARIMAX prediction model were measured to be 82.89% and 80.12% respectively. Then bringing this model into the basketball game, we get its prediction accuracy: 77.12% and 76.21%. A random forest analysis method was used to evaluate the significance of each variable in prediction. The significance of each indicator is as follows: 0.33, 0.26, 0.17, 0.15 and 0.09.