Pattern Recognition for Tennis Tactics using Hidden Markov Model from Rally Series

Abstract

In this paper, we propose pattern recognition for tennis tactics using ball trajectory data from motion capture system. The purpose of the study is to adapt machine learning in order to implement feature extraction of rallies in tennis game using positions of ball bounce. We modeled this task as time-series data statistical modeling based on the Hidden Markov Model. We also conducted experiments and we verified the dispersion of the mixture component and the centroid, corresponding to four types of tennis court area division. Moreover, we implemented feature extraction of rally according to the initial state probability and the state transition probability.