Prediction model and technical and tactical decision analysis of women’s badminton singles based on machine learning - Yuan, Hanguang; Wang, Yaodong; Yang, Kairan; Bin, Yulu (PloS one, 2024)
In the Paris Olympic cycle, South Korean women’s athlete An Se-young rose to the top of the 2023 BWF Olympic points with a win rate of 89.5%. With An Se-young as the subject, this paper aims to carry out technical and tactical analysis of women’s badminton singles and formulate a prediction model based on machine learning. Firstly, An’s technical and tactical statistics are analyzed and presented in a proposed "three-stage" data classification method. Secondly, we improve our “three-stage” machine learning dataset using video analysis of 10 matches (21 point games) where An Se-young faced off against four other players ranked in the top five of the World Badminton Federation (BWF) in week 44 of 2023. Finally, we establish a prediction model for the scoring and losing of points in the women’s badminton singles based on the ‘Decision tree’, ‘Random forest’, ‘XGBoost’, ‘Support vector’ and ‘K-proximity’ algorithms, and analyze the effectiveness of this model. The results show that the improved data classification is reasonable and can be used to predict the final score of a match. When the support vector machine uses the RBF function kernel, the accuracy reaches its highest at 87.5%, and the consistency of this prediction model is strong. An’s playstyle is sustained and unified; she does not seek continuous pressure, but rather exploits and maximizes her aggression following any mistake made by her opponents, immediately utilizing assault methods such as kills or dives, often resulting in the conversion of points during the subsequent 2–3 strikes.