First we need to calculate the levels of competition
-C-levels- for all seasons in Formula 1:

This is done by:  

• applying the standard deviation (stdev.p) of points per race* earned by the first 6 drivers of each F1 season

• inversion of the result:
  1 / stdev.p**

Next, the level of competition for each driver in his entire career is calculated by taking the average of all the C-levels from the years in which a driver has been active.

This is called C-lever driver

Finally, the C-level driver is divided by the average of all C-levels year to yield the 

C-factor:

OneFormula uses a tool that defines the level of competition a driver has faced in his career:
the C-factor.

With it, drivers from different decades can be compared with each other in a correct way.

If the average of C-levels within a driver's career years is higher than the average of all C-levels from 1950-2024, the C-factor is > 1. Same applies for the opposite.

Consequently, a driver's score will be adjusted accordingly.

Examples of high and low
C-factors are: 

This brings us to the final formula:

ds = (3wi + 2pp + 1pd) x cf

ds = driver score

wi = % wins

pp = % pole positions

pd = % podiums

cf = c-factor

• formula1points.com offers an interesting approach, whereby visitors can select from a number of criteria and their weighting factors. Based on the selection, the site produces a ranking. Using the same criteria and weighting factors, the ranking appears similar to the OneFormula ranking   

• Stats F1 is used as the preferred database for the OneFormula model.

• Bell, A., Smith, J., Sabel, C. E., and Jones, K. (2016). Formula for success: multilevel modeling of formula one driver and constructor performance, 1950–2014. Journal ofQuantitative Analysis in Sports, 12(2):99–112.

• Bol, R. (2020). How to win in formula one: is it the driver or the car? The Correspondent.

• Budzinski, Oliver and Feddersen, Arne, Measuring Competitive Balance in Formula One Racing (March 16, 2019). Available at SSRN: https://ssrn.com/abstract=3357687 or http://dx.doi.org/10.2139/ssrn.3357687

• Burkner, P.-C. (2017). brms: An R package for bayesian multilevel models using Stan. Journal of statistical software, 80(1):1–28.

• Eichenberger, R. and Stadelmann, D. (2009). Who is the best formula 1 driver? An economic approach to evaluating talent. Economic Analysis & Policy, 39(3).

• Elo, A. (1978). The rating of chess players, past and present. Arco, New York.

• Henderson, D. A., Kirrane, L. J., et al. (2018). A comparison of truncated and time-weighted Plackett–Luce models for probabilistic forecasting of formula one results. Bayesian Analysis, 13(2):335–358.

• Ingram, M. (2021). A first model to rate formula 1 drivers. https://martiningram.github.io/f1-model/ (accessed March 2022).

• Phillips, A. J. (2014). Uncovering formula one driver performances from 1950 to 2013 by adjusting for team and competition effects. Journal of Quantitative Analysis in Sports,10(2):261–278.

• Van Kesteren, E.-J. and Bergkamp, T. L. G. (2022). Code Repository: Bayesian Analysis ofFormula One Race Results.