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.

翻译：一级方程式的成功表现由驾驶员的驾驶技术和赛车车队优势共同决定。这使得评估这项运动的关键表现问题变得困难，例如：谁是顶尖的一级方程式车手？最佳车队是哪一个？以及他们对成功的相对贡献如何？本文基于一级方程式混合动力时代（2014-2021赛季）的数据回答这些问题。我们提出了一种新颖的贝叶斯多层次排序逻辑回归方法来建模个人比赛完赛名次。结果表明，我们的建模方法能够很好地拟合数据，从而实现对驾驶技术和车队优势的精确推断。我们得出结论：汉密尔顿和维斯塔潘是混合动力时代最优秀的车手，梅赛德斯、法拉利和红牛三大车队明显优于其他车队，约88%的比赛成绩方差可由车队因素解释。我们认为这种建模方法可能对一级方程式以外的体育项目也有借鉴意义，因为它能为构成成功的独立因素构建表现评级。