  Two natural ways of modelling Formula 1 race outcomes are a probabilistic
approach, based on the exponential distribution, and statistical regression
modelling of the ranks. Both approaches lead to exactly soluble race-winning
probabilities. Equating race-winning probabilities leads to a set of equivalent
parametrisations. This time-rank duality is attractive theoretically and leads
to new ways of dis-entangling driver and car level effects as well and a
simplified Monte Carlo simulation algorithm. Results are illustrated by
applications to the 2022 and 2023 Formula 1 seasons.

Applications;Probability