The Persistence of the Dirty Air Penalty: A Causal Analysis of Formula 1's 2022 Ground Effect Regulations
脏空气惩罚的持续性:F1 2022 年地面效应规则的因果分析AI 解读 ↓
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脏空气惩罚的持续性:F1 2022 年地面效应规则的因果分析AI 解读 ↓
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In early 2022, FIA mandated the change to the technical regulations for Formula 1, requiring to design cars around ground effect aerodynamics to mitigate the performance loss incurred by driving in the turbulent "Dirty Air" of another car. The reduction of the penalty was subsequently claimed, but never formally evaluated on a per lap basis. We gathered 100486 racing laps for the years 2021-2025 using the FastF1 API, built a compound-specific physics correction for Dirty Air to account for fuel and tyre wear, and evaluated the change introduced by the 2022 regulation using both OLS regression with clustered standard errors and Causal Forest Double/Debiased Machine Learning. We repeated the analysis using season as a categorical variable, applying a generalized circuit filter to account for a changing calendar. Both methods produced similar results, demonstrating no statistically significant change to the Dirty Air penalty when combining 2022-2025 data together (OLS interaction term beta = -0.0025, p = 0.803; Causal Forest mean effects of 0.019s and 0.018s per second of following distance in 2021 and 2022-2025). We tested the hypothesis, that the penalty reduction may have been concentrated in 2022 and partially eroded through 2023-2025. We found no season for which the change was different from the one observed in 2021 in the OLS analysis, while several seasons were significantly different from zero in the Causal Forest analysis, in a non-monotonic pattern, not following the theorized erosion timeline. We conclude there was no statistically significant change to the Dirty Air penalty under the new regulation, both in pooled and season-specific analysis. By evaluating the real-world racing results, we demonstrate that even if an aerodynamic advantage exists, it did not translate into a measurable improvement over following distance, where other factors can swamp such an effect.