Special Physical Characteristic and Training Strategy of Badminton Based on Machine Learning Algorithm

Abstract

Machine learning algorithms involve many fields such as statistics and probability theory. It is mainly aimed at computer simulation or execution of human learning behaviors to acquire new knowledge and skills. And the existing knowledge is to be integrated and then continue to be improved. This study aims to introduce a machine learning algorithm to establish a corresponding training effect evaluation model, to use the model to research and analyze the special physical fitness training strategy of badminton, and to discuss the appropriate badminton physical fitness training method. At the same time, the Markov model is used to compare the effect of the machine learning algorithm model. The experimental results show that the combination of four kinds of training of phosphate energy supply capacity, speed, sensitivity, and core strength can improve the training performance of badminton players by 10 points and the training efficiency by 4%. Therefore, it is necessary to comprehensively and reasonably arrange the four physical training methods to achieve better training effects. The average evaluation and prediction accuracies of the machine learning algorithm model were 86% and 87%, respectively, and the computing time was 26.7 ms and 13.6 ms. The average evaluation and prediction accuracies of the Markov model were 79% and 82%, respectively, and the computing time was 19.5 ms and 11.8 ms. It can be seen that the model based on the machine learning algorithm is more accurate than the Markov model.

Reinforcement Learning Algorithm to Optimize Players' Tactical Decisions and Round Planning in Tennis Matches

“…For example, it can recognize when certain tactics are particularly effective against specific opponents or in particular match situations. During matches, the reinforcement learning algorithm would continuously assess the evolving dynamics on the court, updating its strategies in real-time based on the feedback received from each decision made [15]. For instance, if a player consistently loses points when approaching the net against a baseline specialist, the algorithm may recommend adjusting the strategy to maintain a defensive baseline position instead [16].…”

Retracted: Special Physical Characteristic and Training Strategy of Badminton Based on Machine Learning Algorithm

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”