Department of Physical Education, University of Mining and Technology (Beijing), Beijing, China.
College of Physical Education, Hunan Normal University, Hunan, China.
PLoS One. 2024 Nov 14;19(11):e0312801. doi: 10.1371/journal.pone.0312801. eCollection 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.
在巴黎奥运周期，韩国女运动员安洗莹以 89.5%的胜率登顶 2023 年 BWF 奥运积分榜首。本文以安洗莹为研究对象，旨在对女子羽毛球单打进行技术战术分析，并基于机器学习制定预测模型。首先，采用提出的“三段式”数据分类方法对安的技术战术统计数据进行分析和呈现。其次，通过对安在 2023 年第 44 周对阵世界羽联（BWF）排名前五的其他四名选手的 10 场（21 分制）比赛的视频分析，对我们的“三段式”机器学习数据集进行改进。最后，基于“决策树”“随机森林”“XGBoost”“支持向量”和“K-近邻”算法，建立女子羽毛球单打得分和失分预测模型，并分析该模型的有效性。结果表明，改进的数据分类合理，可用于预测比赛的最终比分。支持向量机使用 RBF 函数核时，准确率最高可达 87.5%，且该预测模型的一致性较强。安的打法持续且统一；她不寻求持续施压，而是在对手犯错后利用和最大化自己的攻击性，立即采用杀球或扑球等攻击方式，通常在随后的 2-3 次击球中转化为得分。
