Design and Adjustment of Optimizing Athletes' Training Programs Using Machine Learning Algorithms

Abstract

The adjustment of optimizing athletes' training programs using machine learning involves leveraging data-driven approaches to enhance training regimens and performance outcomes for athletes. By analyzing various factors such as athletes' physiological data, training logs, performance metrics, and external conditions, machine learning algorithms can identify patterns, correlations, and optimal training strategies. These insights enable coaches and sports scientists to tailor training programs more effectively to individual athletes' needs, goals, and abilities. By continuously adapting and refining training plans based on real-time feedback and data analysis, machine learning helps optimize athletes' preparation, recovery, and overall performance, ultimately maximizing their potential and success in competitive sports. This paper explores novel methodologies and machine-learning techniques aimed at optimizing athletes' training programs. With the increasing demand for peak performance and injury prevention in sports, there is a growing need for data-driven approaches to tailor training regimens effectively. One such methodology, the Optimized Adjustment Evolutionary Computing Feature Selection (OA-EC-FS), is investigated for its ability to select relevant features crucial for enhancing athletes' performance across various sports disciplines. Additionally, machine learning algorithms are employed to classify athletes' training programs based on selected features, enabling coaches and trainers to make informed decisions to maximize performance outcomes.