一级方程式赛车比赛结果的贝叶斯分析：区分车手技能与制造商优势
Bayesian Analysis of Formula One Race Results: Disentangling Driver Skill and Constructor Advantage
AI总结：
本文提出贝叶斯多层次排序逻辑回归模型，基于2014-2021赛季F1数据，区分车手技能与制造商优势，发现汉密尔顿和维斯塔潘最佳，前三车队领先，制造商解释约88%方差。
摘要：
在一级方程式赛车中，成功的表现由车手的技能和赛车的制造商优势共同决定。这使得该运动中的关键性能问题难以回答。例如，谁是最好的F1车手，哪个是最好的制造商，以及他们对成功的相对贡献是什么？在本文中，我们基于一级方程式赛车混合动力时代（2014-2021赛季）的数据来回答这些问题。我们提出了一种新颖的贝叶斯多层次排序逻辑回归方法，用于对个人比赛完赛位置进行建模。我们表明，我们的建模方法很好地描述了我们的数据，从而能够对车手技能和制造商优势进行精确推断。我们得出结论，汉密尔顿和维斯塔潘是混合动力时代最好的车手，前三名的车队（梅赛德斯、法拉利和红牛）明显优于其他制造商，并且大约88%的比赛结果方差由制造商解释。我们认为，这种建模方法可能对一级方程式赛车以外的运动也有用，因为它为促成成功的独立组成部分创建了性能评级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
