Prediction Method of Basketball Players’ Shooting Rate Based on Quantum Search Algorithm

Abstract

The traditional basketball players’ shooting rate prediction algorithm adopts quantum algorithm, which is easy to cause information divergence and fall into local extremum when dealing with the statistical data of large scale basketball players’ shooting rate, the accuracy of prediction of shooting rate of basketball players is not high. An adaptive learning based quantum evolutionary search (QEP) algorithm is proposed to predict the shooting rate of basketball players. The statistical data regression model of basketball players’ shooting rate is constructed, and the statistical characteristic sequence analysis of basketball players’ shooting rate is carried out. The quantum optimization algorithm is used to compare whether the frequency fluctuation in the information flow is the same or not in the statistical regression analysis of shooting hit rate, the same one is merged, and the quantum search algorithm is designed based on the fuzzy C-means clustering algorithm. Based on the adaptive learning quantum evolutionary search algorithm, the statistical data of basketball players’ shooting rate are projected into the high-dimensional phase space, and the real-time prediction optimization of the basketball players’ shooting rate is carried out. The simulation results show that the algorithm has higher accuracy and less error in predicting the shooting rate of basketball players, and it has a better application value in basketball training.