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In sports, martial arts and other interpersonal sport games, skilled players often predict the next move and other actions based on the actions of their opponents. In badminton, players predict the return of a shuttlecock based on information, such as opponent location, the impact point or the racket surface direction. The more experienced a player plays badminton, the more intuitive he or she becomes in predicting the return strokes. In addition, the rally speed of badminton has been increasing year by year in recent years due to improvements in athletic ability resulting from experience, improvements in the performance of rackets and shoes, and physical modification through efficient science-based practice. Badminton has become the fastest ball game in the world. In badminton games, in which shuttlecocks fly around at such high speeds, it becomes extremely difficult to accurately predict the next shuttlecock destination. If the return position of the shuttlecock can be predicted, it will not only make it easier to formulate strategies to beat opponents, but also make it possible to visualize information on how players can overcome their weaknesses. In this paper, we propose a method of predicting where a player will hit a smash depending on his opponent location by applying machine learning to the positional information of the player and the shuttlecock. We also propose a method to predict where the opponent will hit the shuttlecock next by using the machine learning model obtained by this method and to predict whether the opponent can return the smash or not. |
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Machine learning
Data modeling
Neural networks
Data conversion
