Faster identification of faster Formula 1 drivers via time-rank duality
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866910493824778240 |
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| author | Fry, John Brighton, Tom Fanzon, Silvio |
| author_facet | Fry, John Brighton, Tom Fanzon, Silvio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14637 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Faster identification of faster Formula 1 drivers via time-rank duality Fry, John Brighton, Tom Fanzon, Silvio Applications Probability 62P25, 91-10, 62M99, 65C05 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. |
| title | Faster identification of faster Formula 1 drivers via time-rank duality |
| topic | Applications Probability 62P25, 91-10, 62M99, 65C05 |
| url | https://arxiv.org/abs/2312.14637 |