Benchmarking Formula 1 results using a normal model

TL;DR

This paper introduces a statistical normal model approach to distinguish skill from luck in Formula 1, analyzing driver and team performance to better understand competitive dynamics.

Contribution

It presents a novel application of univariate and bivariate normal models to quantify performance expectations at driver and team levels in Formula 1.

Findings

Elite teams consistently outperform non-elite teams

Normal models effectively separate skill and luck effects

Performance expectations align with race outcomes

Abstract

There is enduring interest in disentangling the effects of skill and luck in sport. A key issue in Formula 1 is distinguishing between car-level and driver-level effects. Four elite teams currently dominate Formula 1 and have won every major race for the last four years. In this paper we use univariate and bivariate normal models to quantify reasonable performance expectations at both driver and team levels, distinguishing between elite and non-elite teams. We illustrate our approach with an application to the last fully completed 2025 season.