基于机器学习的分析篮球的防守策略,使用球员运动数据
1Department of Physical Education, Suzhou University of Science and Technology, Suzhou, 215009, Jiangsu, China. ljl@usts.edu.cn.
Scientific reports
|April 22, 2025
概括
本研究介绍了一种使用长短期记忆 (LSTM) 和卷积神经网络 (CNN) 来分析篮球防守策略的混合AI模型. 该模型准确地识别了切换和陷游戏,为教练提供了宝贵的见解.
科学领域:
- 运动分析 运动分析
- 体育运动中的人工智能
- 机器学习用于战略分析和分析.
背景情况:
- 传统的篮球策略分析依赖于手动观察和有限的数据.
- 追踪技术的进步使人工智能应用能够获得更深入的战略见解.
- 了解团队的防守行为对于提高绩效至关重要.
研究的目的:
- 开发一种混合人工智能模型,将LSTM和CNN结合起来,用于分类和分析防守篮球策略.
- 使用大规模跟踪数据,专门识别交换机和陷游戏.
- 为教练和球员提供一个工具来评估防守战术.
主要方法:
- 开发了一种混合的长短期记忆 (LSTM) 和卷积神经网络 (CNN) 模型.
- 使用了国家篮球协会 (NBA) SportVU 的跟踪数据 (超过 32000 个物品).
- 集成的LSTM用于时间运动特征和CNN用于空间球场模式;标准化的球员位置和重新格式化的半场数据成网格.
主要成果:
- 混合型号在识别开关和陷中获得了91.4%的准确性,超过了独立的LSTM和CNN模型.
- 对空间密度和时间序列的分析揭示了防御结构的变形.
- 该模型在区分需要开关和陷的情况方面显示出了显著的好处.
结论:
- 混合LSTM-CNN模型成功地对防守篮球运动进行了分类,为战术评估提供了强大的工具.
- 监督机器学习模型对于体育运动中的实时战术分析至关重要.
- 这项研究为篮球中自动化战略评估和游戏规划奠定了基础.
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