澳大利亚足球比赛结果的实时预测
Mitchell F Aarons1, Chris M Young1, Lyndell Bruce1
1Centre for Sport Research, Deakin University, Geelong, Australia.
Journal of sports sciences
|September 21, 2023
概括
机器学习模型使用技术性能数据准确预测澳大利亚足球比赛结果,帮助实时的教练决策. 这些模型为战略调整提供了有价值的匹配见解.
科学领域:
- 运动分析 运动分析
- 运动中的机器学习
- 澳大利亚足球 澳大利亚足球
背景情况:
- 预测澳大利亚足球比赛结果对于教练和球迷的参与至关重要.
- 现有的方法通常依赖于得分边际,这可能无法捕捉动态的比赛表现.
- 使用技术性能指标的实时预测可以提供更深入的见解.
研究的目的:
- 评估基于技术性能的机器学习模型来预测澳大利亚足球比赛结果.
- 评估这些模型在比赛期间实时决策支持方面的有效性.
- 为了比较模型培训的数据驱动和数据信息化的特征集.
主要方法:
- 利用了来自829场澳大利亚足球联盟比赛 (2017-2021) 的168个技术绩效指标数据库.
- 训练和评估了六种机器学习模型 (GLM,随机森林,AdaBoost) 使用数据驱动和数据信息化的功能.
- 预测每场比赛中的120个正常化时间时期的比赛结果 (胜负).
主要成果:
- 模型的平均分类准确度在73.5-75.8%之间,与基于基准分数的模型 (77.4%) 相比.
- 从比赛开始 (45.7-48.8%) 到比赛结束 (87.2-92.7%),准确度显著增加.
- 数据为基础的特征集通常表现优于数据驱动的特征集.
结论:
- 使用技术性能数据的机器学习模型可以有效地预测实时的澳大利亚足球比赛结果.
- 这些模型为教练提供了一个可行的工具,用于在比赛中为教练提供决策支持.
- 模型选择取决于实际考虑,如时间成本权衡和感知价值.
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