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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.00200 |
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| _version_ | 1866916874960240640 |
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| author | Rane, Saurabh |
| author_facet | Rane, Saurabh |
| contents | Formula 1 performance is a combination of the car's ability and the driver's ability. While a given race or season can tell you how well a car and driver performed jointly, isolating the individual impact of the driver and constructor remains challenging. This paper extends a Regularized Adjusted Plus Minus (RAPM) methodology (Sill 2010), commonly used in basketball and hockey, to parse out individual driver and constructor impact. It employs a time-decayed ridge regression with LOESS (Jacoby 2000) smoothing to predict race results for the Hybrid Engine Era (2014 - 2024). By measuring the constructor and driver coefficients over time, we measure the relative individual impact of driver and constructor throughout the period. Results show that constructors explain 64.0% of the variance in race outcomes in the Hybrid Engine Era. Additionally, constructors have increased importance in benchmarked rank-agnostic cohorts (e.g., Top 10 points finishers) and decreased importance in qualifying. By decomposing performance into individual driver and constructor metrics, we create a robust framework for inter-constructor driver comparisons that the Formula 1 points system obfuscates. Our work enhances the understanding of driver and constructor contributions to race success, offering valuable insights for strategic decision-making in Formula 1. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling Rane, Saurabh Applications Formula 1 performance is a combination of the car's ability and the driver's ability. While a given race or season can tell you how well a car and driver performed jointly, isolating the individual impact of the driver and constructor remains challenging. This paper extends a Regularized Adjusted Plus Minus (RAPM) methodology (Sill 2010), commonly used in basketball and hockey, to parse out individual driver and constructor impact. It employs a time-decayed ridge regression with LOESS (Jacoby 2000) smoothing to predict race results for the Hybrid Engine Era (2014 - 2024). By measuring the constructor and driver coefficients over time, we measure the relative individual impact of driver and constructor throughout the period. Results show that constructors explain 64.0% of the variance in race outcomes in the Hybrid Engine Era. Additionally, constructors have increased importance in benchmarked rank-agnostic cohorts (e.g., Top 10 points finishers) and decreased importance in qualifying. By decomposing performance into individual driver and constructor metrics, we create a robust framework for inter-constructor driver comparisons that the Formula 1 points system obfuscates. Our work enhances the understanding of driver and constructor contributions to race success, offering valuable insights for strategic decision-making in Formula 1. |
| title | Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling |
| topic | Applications |
| url | https://arxiv.org/abs/2508.00200 |