F1 aero expert gives HOPE to new overtaking regulations

The 2022 ground effect regulations have ushered in new beginnings for Formula 1.

A significant focus on ground effect aerodynamics was touted to allow closer racing with the potential for more overtakes.

Simulations of these new regulations showed a significant reduction in the dirty air produced by the leading car that would mitigate downforce loss by 28 per cent (a 46 per cent loss in 2021 compared to an 18 per cent loss in 2022 and beyond) allowing cars to stay closer together and therefore, increasing the probability of overtaking.

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It is now two years into the new regulations and we have asked: are they working as expected?

How do we measure the impact of the new regulation changes on racing

We evaluated the new regulations by two criteria: 1) The total number of overtakes made by cars, demonstrating that cars could remain closer to each other to fight for position; and 2) the distribution of finishing positions for each constructor.

These metrics tell us how closely cars can follow each other in a given race on average and assess the degree to which teams can vie, with more equal probability, for positions higher up on the grid. We assume that overtakes are an output measure of the closeness of cars during a race.

What does the data show?

We started by analysing the number of overtakes between the end of the turbo hybrid era to the ground effect era i.e. 2019-2023 (12 races so far in 2023).

We analysed a total of four circuits so that we could compare overtakes per circuit fairly across years and include the 2023 campaign. We found that, on average, the new regulations have seen 92 more overtakes compared with the turbo hybrid era regulation set we analysed.

This data suggests that cars are able to follow more closely to execute an overtake over the course of the race.

Can More Cars Fight For Points?

Next, we asked whether the number of overtakes have translated into an equal fight for the top positions on the grid for each constructor.

We analysed the expected finishing positions of a constructor compared with the observed finishing positions for each constructor after 12 rounds of racing to compare the 2023 season fairly.

We compared the distribution of Formula 2, a spec series where there would be a broader distribution of finishing positions across teams, and compared the distribution of a low inter-team competition scenario where cars would finish roughly in the order of their machine competitiveness.

We then performed a Chi-square goodness of fit test to measure how well the expected distribution (in our case, equal chance of finishing in higher positions such as in F2 compared or to a low inter-team competition scenario in which all teams have a more unequal chance of fighting for higher positions to what we have observed thus far in 2023 across teams).

We found that F1 in 2023, is still closer to a lower inter-team competition model, which has been true since the turbo-hybrid era as well —where teammates tend to finish closer together and finishing position is still largely dependent on machinery.

As expected, we found that F1 distributions are significantly different to a spec series like F2.

Conclusion

So what can we conclude? Are the regulations actually working? In a word, yes.

We are witnessing more overtakes, suggesting closer racing in this new regulation set—which was the goal of the 2022 changes. However, there is still not sufficient opportunity for all teams to vie for top positions based on the distribution of finishing positions per team.

But, we are only just at the beginning. F1 will never be a spec series like Formula 2. Still, the signs are there for F1, together with the cost cap and new ground effect aerodynamic regulations, to shift the balance towards more teams not only following closely and making overtakes, but securing top 10 positions, with closer to equal probability.

Stay tuned, it's year two into the new regulations and there is certainly more to come.

Shubham Sangodkar is a former F1 Aerodynamicist with a Master's in Racing Car Design specialising in F1 Aerodynamics and F1 Data Analysis. He also posts aerodynamics content on his YouTube channel, which can be found here.

CodeF1 is a freelance data scientist using machine learning/AI, game theory and data analytics to understand Formula 1 data. You can follow them on Instagram, Twitter and Youtube, which can be found here.

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