对一级方程式赛车结果的贝叶斯分析:解开驾驶员技能和制造商优势
Erik-Jan van Kesteren1, Tom Bergkamp2
1Methodology & Statistics, Utrecht University, Utrecht, Netherlands.
概括
本研究使用新的贝叶斯模型确定了混合时代 (2014-2021) 最好的一级方程式车手和制造商. 它揭示了制造商性能显著影响比赛结果,解释了88%的差异.
科学领域:
- 运动分析 运动分析
- 统计建模 统计建模
- 赛车运动科学 汽车运动科学
背景情况:
- 一级方程式的表现依赖于驾驶员的技能和制造商的优势,这使得单个贡献难以孤立.
- 关键问题包括确定最好的车手和制造商,并量化它们对比赛成功的影响.
研究的目的:
- 在混合时代 (2014-2021) 量化评估一级方程式中的驾驶员技能和制造商优势.
- 开发和应用一种新的统计方法来分析比赛表现数据.
主要方法:
- 开发了贝叶斯的多层次排序逻辑回归模型来分析个别比赛的终点位置.
- 该模型应用于2014-2021年一级方程式混合时代赛季的数据.
主要成果:
- 该模型准确地描述了比赛结果数据,使得驾驶员和制造商的表现能够得到精确的推断.
- 路易斯·汉密尔顿和马克斯·维斯塔被确定为顶级车手.
- 梅赛德斯,法拉利和红牛是领先的制造商.
- 制造商优势约占比赛结果差异的88%.
结论:
- 该研究提供了一种强大的方法来排名F1中的车手和制造商.
- 施工人员的表现是影响比赛结果的主要因素.
- 该方法可以扩展到分析其他竞技运动中的表现.
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