Abstract
After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two-arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front-row 4th position strong attack and the back-row 6th position for comparison and analysis, and analyze the process from the take-off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows ( P < 0.05 ). The discharge volume of the left gluteus maximus during the front-row spiking process is greater than that of the back-row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.
Highlights
In a large amount of text and image information, if manual methods are used to extract useful information for volley data analysis, the enormous workload and slow speed are undoubtedly unrealistic
The paired-sample t-test is used to obtain type A left and right hip angles and left and right knees; there was no significant difference between the angle and the left and right ankle angles (P > 0:05); for type B, the angle of the right joint was mainly seen
The buffering time of type B technology is much longer than that of type A technology. This shows that type B technology has sufficient time for buffering
Summary
In a large amount of text and image information, if manual methods are used to extract useful information for volley data analysis, the enormous workload and slow speed are undoubtedly unrealistic. If you can use the excellent performance of the computer to complete the task, it is definitely the most effective solution; you can use machine learning to analyze volleyball data and use it in sports and other directions [1]. Regardless of the aspect, the key to volleyball data analysis is kinematics analysis and industrial analysis. With the application of the scoring method for each ball, the volleyball game is more intense than the previous game, the pace is faster, and the projection is stronger, more exciting, and exciting, attracting a large number of loyal fans. Because the direction of the ball is Wireless Communications and Mobile Computing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
