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Two natural ways of modelling Formula 1 race outcomes are a probabilisticapproach, based on the exponential distribution, and statistical regressionmodelling of the ranks. Both approaches lead to exactly soluble race-winningprobabilities. Equating race-winning probabilities leads to a set of equivalentparametrisations. This time-rank duality is attractive theoretically and leadsto new ways of dis-entangling driver and car level effects as well and asimplified Monte Carlo simulation algorithm. Results are illustrated byapplications to the 2022 and 2023 Formula 1 seasons.

Time-rank duality in a simple model for Formula 1 racing