Bayesian Analysis of Formula One Race Results: Disentangling Driver Skill and Constructor Advantage
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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 Beta regression method to model individual race success as the proportion of outperformed competitors. 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 around 86 argue that this modeling approach may prove useful for sports beyond Formula One, as it creates performance ratings for independent components contributing to success.
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