AI 中文总结
该研究通过分析2021-2025年F1的10万余圈数据，用OLS回归和因果森林机器学习发现2022年F1地面效应规则未显著降低脏空气惩罚，且无理论预期的侵蚀模式。
AI 中文摘要
2022年初，国际汽车联合会（FIA）强制要求对一级方程式赛车（Formula 1）的技术规则进行修改，要求赛车围绕地面效应空气动力学进行设计，以减轻在另一辆赛车产生的湍流“脏空气”中行驶所带来的性能损失。此后有人声称该惩罚已减少，但从未在单圈基础上进行正式评估。我们使用FastF1 API收集了2021年至2025年的100486个赛车圈数据，构建了针对脏空气的复合特定物理校正，以考虑燃油和轮胎磨损，并使用带有聚类标准误的普通最小二乘法（OLS）回归和因果森林双/去偏机器学习，评估2022年规则带来的变化。我们将赛季作为分类变量重复分析，应用通用赛道过滤器以应对赛历的变化。两种方法得出了相似结果，表明将2022-2025年数据合并后，脏空气惩罚没有统计学意义上的变化（OLS交互项β=-0.0025，p=0.803；因果森林在2021年和2022-2025年的跟车距离每秒对应的平均效应分别为0.019秒和0.018秒）。我们检验了“惩罚减少可能集中在2022年，并在2023-2025年部分侵蚀”的假设。OLS分析中，我们发现没有任何赛季的变化与2021年观察到的变化存在差异，而因果森林分析中则有多个赛季与零存在显著差异，呈现非单调模式，与理论上的侵蚀时间线不符。我们得出结论，无论是合并分析还是分赛季分析，新规则下脏空气惩罚均无统计学意义上的变化。通过评估真实世界的赛车结果，我们证明即使存在空气动力学优势，也未转化为跟车距离上可测量的改善，其他因素会淹没这种效应。
英文摘要
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.
Comments10 pages, 2 figure. Code and data available at https://github.com/BhavayJ-here/The-Persistence-of-the-Dirty-Air-Penalty--Research-Paper
