论文标题
贝叶斯对一级方程式赛车的分析结果：拆除驾驶员技能和构造函数优势
Bayesian Analysis of Formula One Race Results: Disentangling Driver Skill and Constructor Advantage
论文作者
论文摘要
一级方程式的成功性能取决于驾驶员的技能和赛车构造函数优势。这使得这项运动中的关键表现问题难以回答。例如，谁是最好的一级方程式驱动程序，哪个是最好的构造函数，他们对成功的相对贡献是什么？在本文中，我们根据一级方程式赛车（2014-2021季节）的混合时代的数据回答这些问题。我们提出了一种新颖的贝叶斯多级等级排序logit回归方法，以建模个别种族饰面位置。我们表明，我们的建模方法很好地描述了我们的数据，从而可以进行有关驾驶员技能和构造函数优势的精确推断。我们得出的结论是，汉密尔顿和Verstappen是混合时代的最佳动力，前三名球队（梅赛德斯，法拉利和红牛）显然优于其他构造函数，大约88％的比赛结果差异由构造师解释。我们认为，这种建模方法可能对一级方程式赛车超出一级方程式而有用，因为它为有助于成功的独立组件创建了绩效评级。
Successful performance in Formula One is determined by combination of both the driver's skill and race-car constructor advantage. This makes key performance questions in the sport difficult to answer. For example, who is the best Formula One driver, which is the best constructor, and what is their relative contribution to success? In this paper, we answer these questions based on data from the hybrid era in Formula One (2014 - 2021 seasons). We present a novel Bayesian multilevel rank-ordered logit regression method to model individual race finishing positions. We show that our modelling approach describes our data well, which allows for precise inferences about driver skill and constructor advantage. We conclude that Hamilton and Verstappen are the best drivers in the hybrid era, the top-three teams (Mercedes, Ferrari, and Red Bull) clearly outperform other constructors, and approximately 88% of the variance in race results is explained by the constructor. We argue that this modelling approach may prove useful for sports beyond Formula One, as it creates performance ratings for independent components contributing to success.
