开发一种新的预期进球指标来量化虚拟现实足球进球应用程序中的表现,称为CleanSheet
Matthew Simpson1, Cathy Craig2
1School of Maths & Physics, Queens University Belfast, Belfast BT7 1NN, UK.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究介绍了CSxG,这是虚拟现实 (VR) 目标保持培训应用程序的新指标. 它通过分析球的飞行和定位来测量射门难度,改进了性能分析,超出了简单的保存率.
科学领域:
- 运动科学 运动科学 运动科学
- 人与计算机的交互
- 数据科学数据科学数据科学
背景情况:
- 虚拟现实 (VR) 体育训练应用程序对于运动员的发展越来越受欢迎.
- 目前的VR进球应用程序,如CleanSheet缺乏细微的性能指标,主要跟踪保存率,而不考虑射门难度.
- 有抱负的守门员需要更复杂的工具来有效地使用VR技术进行训练.
研究的目的:
- 开发一种新的射门难度度指标,称为CSxG (CleanSheet Expected Goals),用于VR守门员训练.
- 增强从VR游戏中获得的性能数据的意义.
- 在VR环境中提高守门员表现分析的准确性.
主要方法:
- 开发了一个回归模型,将现有的预期目标 (xG) 模型,守门员表现指标和心理学研究结合起来.
- 使用CleanSheet VR应用程序中的用户保存率数据作为目标变量.
- 与球飞行和进球位置相关的内置输入变量,包括从Tau理论中要求的关闭率 (RROC).
主要成果:
- 开发的CSxG模型将所需的完成率 (RROC) 确定为被承认目标的最重要的预测指标.
- 验证表明CSxG准确地预测了极端射击难度,但对中等范围的难度得分 (0.4-0.8) 的准确性较低.
- 确定了用于未来模型改进的构建游戏和进球杆尺寸等额外因素.
结论:
- CSxG指标提供了一种更先进的方法来评估门将在VR训练中的表现.
- 这项研究推进了体育分析中的预测建模,特别是基于VR的训练.
- 这些发现支持了通过VR技术增强门将训练策略和绩效发展的潜力.
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