Basketball Data Analysis Based on Spark Framework and K-means Algorithm

Abstract

With the extensive development of basketball, traditional data processing and analysis methods can no longer satisfy the optimization of basketball training indicators and the evaluation of training status. This severely restricts the digitization and management of basketball information data in our country. At present, my country’s basketball big data analysis still faces the following problems: (1) At present, there is still a lack of research on the platform and frame application of data calculation and analysis in the field of basketball sports. As the modern computer data storage and collection facilities, the information processing equipment and functions of sports data are becoming more and more perfect, the workload of information storage and calculation of basketball sports data continues to expand. The existing sports data analysis service platform is far from being able to meet the needs of modern basketball big data analysis technology. (2) Because people's understanding of deep learning modeling and machine learning algorithms is not deep enough, it is limited to the query of individual action data and the statistical description of the competitiveness level, and the data mining and analysis are not deep enough. And it is unable to provide effective support for my country’s basketball sports information prediction and strategy implementation [1].