Application of Machine Learning and Digital Information Technology in Volleyball

Abstract

Since the 1980s, machine learning has attracted extensive attention in the field of artificial intelligence. Following the expert system, it opened a precedent for the application of machine learning in the field of artificial intelligence and became one of the important topics of artificial intelligence. However, in the field of volleyball, the application of machine learning and information technology in volleyball is extremely limited. Volleyball has not developed widely in society nor has it become a common event in people’s daily life. Therefore, the development of volleyball in China lags behind. Unlike other sports, volleyball requires both strong skills and playing tactics. While taking into account the technical and tactical aspects, the requirements for the comprehensive quality and learning ability of both sides of the teaching are relatively strict. If the application of modern information technology is neglected, it may affect the teaching effect of volleyball and hinder the long-term spread of volleyball. The article starts with the serving, landing, and blocking of two groups of volleyball players with different sports levels. Through the application of machine learning and digital information technology in volleyball, as well as the use of artificial neural networks and genetic algorithms, the reaction time and accuracy of judging serving, landing, and blocking are improved, and specific application strategies are further proposed. According to the influence of athletes of different levels on the cognition of volleyball landing points, it can be seen that there are three parts that account for 40% of the allocation.

The Use of Developing Technology in Sports

“…If we have to define machine learning, we can say that it is computer programs that can improve their processing power by themselves based on experience. When the effect of athletic level of volleyball players on decision making, accuracy, reaction time and cognitive level in hitting, landing, and blocking was analyzed by machine learning method, it was concluded that the higher the athletic level, the less the players were affected by other factors (Yu et al, 2022). However, factors such as motion blur due to ball speed, low camera frequency or noisy ball contour (excessive exposure time causes blurring and stretching of the ball contour), lighting or changing environmental conditions (player and spectator movements in the background) can reduce accuracy.…”

Decision Support System for Optimizing Tactics and Strategies of Sports Competition Using Reinforcement Learning Algorithm

“…The system can learn from past data and patterns, accurately identifying new patterns and movements [18]. The system can also optimize its decision-making processes, considering system performance, resource utilization, and security requirements [19]. In Volleyball, DSS integrated with edge computing, ISCC, optimization, and machine learning can analyze player and team movements, identify strengths and weaknesses, and generate insights for decision-makers [20].…”