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.
Highlights
With the continuous improvement of the competitive level of modern basketball, both sides of the game pay attention to the aspects of technology, tactics, style, consciousness, psychology, and physical fitness and pay great attention to the control and mastery of the rhythm of the game [1, 2]
Edge computing uses the storage and processing capabilities of many IoT devices to connect them to the Internet deployed at the edge, thereby providing an intermediate layer between terminal devices and the cloud [31,32,33]
Edge computing uses the storage and processing capabilities of many IoT devices to connect them to the Internet deployed at the edge, thereby providing an intermediate layer between terminal devices and the cloud [49]
Summary
With the continuous improvement of the competitive level of modern basketball, both sides of the game pay attention to the aspects of technology, tactics, style, consciousness, psychology, and physical fitness and pay great attention to the control and mastery of the rhythm of the game [1, 2]. Each round usually includes events such as dribbling, passing, shooting, and scoring Such a regular video content organization structure brings great convenience to sports video analysis. By analyzing massive basketball game data, it can provide a new way for position efficiency calculation. To solve this problem, we can build a basketball game position efficiency calculation model by combining edge computing and data mining technology classification technology [13, 14]. We build a basketball game position efficiency calculation architecture through edge computing technology.
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