Faster identification of faster Formula 1 drivers via time-rank duality
研究了两种建模F1比赛结果的方法（基于指数分布的概率方法和排名的计量经济学模型），发现两者等价，从而能更快分离车手和赛车水平的影响。
Two natural ways of modelling Formula 1 race outcomes are a probabilistic approach, based on the exponential distribution, and econometric 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 quicker ways of dis-entangling driver and car level effects.