  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.
