Application of Statistical Analysis to the Competition and Training Data

This paper focuses on statistical analysis methods for sports events and athlete training data, including multiple regression models, regression trees, and XGBOOST models. For the big data situation of the training and competition of athletes, this paper explains and expands the principal component analysis methods for denoising and dimensionality reduction. For the above models and techniques, this paper reviews the relevant cutting-edge results of several excellent research papers, compares them horizontally, and analyses them. In addition, based on the strengths and weaknesses of the existing research methods and related conclusions, it points out the research gaps and development potential in the field of sports statistics and gives feasible suggestions to help the progress of sports statistics and the physical function research of athletes.