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.

翻译：两种自然的F1赛车结果建模方法分别是基于指数分布的概率方法和对排名进行统计回归建模。这两种方法都导致可精确求解的获胜概率。令获胜概率相等可导出一组等价参数化方法。这种时间-排名对偶性在理论上颇具吸引力，并衍生出分离车手与赛车级别效应的新途径，以及一种简化的蒙特卡洛模拟算法。通过2022年和2023年F1赛季的应用实例对结果进行了说明。