Application of Big Data Analysis Techniques in Sports Training and Physical Fitness Analysis

Abstract

With the development of sports and information technology, people use mathematical tools and computer technology to study sports data and mine the intrinsic value of sports data. Statistical methods are the most widely used to achieve this goal. The research purpose of sports effect evaluation research is to understand the impact of sports on physical fitness through mining and analysis of sports data and to provide theoretical guidance for the public to participate in fitness activities scientifically and effectively. At present, in the study of combining individual performance test data, the research on the standardization of physical fitness monitoring data for sports training is relatively scarce. Therefore, based on the background of big data, this paper integrates the existing data standardization work and designs a plan for the standardization of physical fitness monitoring data for sports training. Combined with machine learning, data preprocessing is performed to obtain the data required by the machine model. The comprehensive physical fitness rating model and the recommendation model are established to realize the development of physical fitness monitoring service applications. In the experiment, compared with the three classical methods, the results show that the classification accuracy of this paper is 4% higher than that of other algorithms, which can more intuitively reflect the characteristic samples of sports training. In this paper, the data mining and analysis technology based on feature indicators in the mining and application of sports data has great application value for human fitness guidance and has certain research value and market application prospects.