Optimization path of physical training in college soccer teaching based on multivariate statistical analysis

Abstract

Abstract Physical training in college soccer teaching has the problems of poor science and insufficient data management, this paper applies multivariate statistical methods to uncover data associations in training to construct a digital planning method. The digital characteristics of random vectors can be identified through the theoretical analysis of the multivariate statistical analysis algorithm. Correlation analysis is applied to test the validity of the research data, followed by the construction of regression equations of pre-competition functioning on competition fitness, to achieve the statistical examination of the research data. The multivariate statistical analysis algorithm was used to conduct a case study on physical training for college soccer. The results showed that after 16 weeks of soft ladder training the average muscle strength of the left rectus femoris muscle of 35 players in the experimental group increased from 31.17Kg to 33.32Kg (P < 0.01), and the average muscle strength of the right rectus femoris muscle increased from 30.29Kg to 32.37Kg (P < 0.01) There was a highly significant difference. This study provides theoretical references for physical training in college soccer teaching and promotes the development of college sports teaching.