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为羽毛球比赛开发顺序获胜率预测模型:应用专家系统的顺序概率比率测试
1Institute of School Physical Education, Korea National University of Education, Cheongju, Republic of Korea. eunhyejo3@gmail.com.
BMC sports science, medicine & rehabilitation
|March 14, 2025
概括
本研究介绍了六种连续赢得百分比预测的羽毛球模型,使用专家系统连续概率比率测试 (EXSPRT) 实时比赛分析和增强观众参与.
科学领域:
- 运动分析 运动分析
- 计算科学 计算科学
- 预测建模预测建模
背景情况:
- 开发了羽毛球的连续胜率预测模型.
- 使用专家系统的顺序概率比率测试 (EXSPRT).
- 旨在计算事件难度并建立初始先验概率.
研究的目的:
- 开发和评估羽毛球的连续胜率预测模型.
- 计算事件难度并设定匹配中的初始先验概率.
- 通过实时预测来增强观众参与度.
主要方法:
- 分析了2018年BWF赛事的100场男子单打比赛 (222场比赛).
- 根据决定性因素评估了六种事件难度模型.
- 从2019年开始使用30场男子单打比赛 (74场比赛) 进行初始预先概率计算,从oddsportal.com.com获得赔率.
主要成果:
- 评估了六种模型的有效性,使用收集的概率的应用率 (15% - 30%).
- 确定一个初始先前概率反映了25%的选择几率,证明了优越的有效性.
- 成功开发了六种连续获胜率预测模型.
结论:
- 六个基于EXSPRT的顺序赢得百分比预测模型提供实时羽毛球预测.
- 这些模型可以提高观众参与度,并作为其他运动的基础.
- 未来的工作应该集中在一个程序,以确定和实施最有效的模型在实践中.
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