A Method of Using Data Mining and Edge Computing to Calculate the Standing Efficiency of Basketball Games

Abstract

The continuous improvement of basketball tactics has high requirements for athletes’ position selection. This article proposes an intelligent method for basketball position selection. Massive basketball game data will provide people with richer content. Analyzing massive basketball game data can provide a new method for position efficiency calculation. To solve this problem, we can combine edge computing and data mining technology classification technology to build a basketball game position efficiency calculation model. First of all, we build a basketball game position efficiency calculation architecture through edge computing technology. Secondly, we use random forest algorithm and fuzzy neural network algorithm to analyze relevant basketball game information. The experimental simulation test results verify the superior performance of the basketball game position efficiency calculation model established in this paper. This model can provide help to improve the information level of basketball games.