Prediction of Sports Performance and Analysis of Influencing Factors Based on Machine Learning and Big Data Statistics

Abstract

Performance prediction is one of the important contents of sports future development strategy research. This paper conducts research on sports performance prediction and influencing factor analysis based on machine learning and big data statistics. The purpose of forecasting research is to formulate short-term, medium-term, and long-term sports development planning and policy services for decision-makers of sports development. Advantages and disadvantages make sports workers consciously revise and realize their own sports future plans. First, the analysis of the influencing factors of sports performance is proposed, including the setting and scoring of sports examination items, research on standards, training methods for physical examination items, and physical examination learning. Second, the application of big data statistics based on machine learning algorithms is studied. Based on machine optimization algorithms, it summarizes the naive Bayesian classification algorithm and big data statistical classification algorithm. The theoretical idea of rough set algorithm and finally through the comparative analysis experiment based on big data statistics of male and female sports test scores and the comparison experiment of the accuracy of three algorithms to predict the results, it is concluded that the endurance quality of boys is better than that of girls, which indicates the quality of sustainable development. There is a certain gender gap, there are significant differences in the skills of male and female students, and the machine-optimized algorithm can predict sports performance with the highest accuracy among the three algorithms.