About

What's all this about?: A Note on Methodology

First things first... This isn't that other Elo model. You might have seen "How I calculated an ELO rating for every F1 driver ever" on YouTube or Matthew Perron's work. Those are great. They are arguably more "statistically rigorous". They use strict head-to-head teammate comparisons. My model... does not do that. We adopt a different, slightly more abstract set of weighted performance factors. Different tools for different jobs!

The Calculation: How do we compute the numbers?

It's an iterative process. We update it after every Grand Prix. The core concept is the Race Performance Score.

1. Take an arbitrary race rating (e.g. 8.6)

2. Scale it by a multiplier (100)

3. Add a baseline (1000)

4. Compare against the pre-race Elo

5. Multiply the difference by a Weighting Factor (K)

6. Add that to the old Elo.

Implied Ratings: Context is king

Abstract Elo numbers are hard to visualize. What does 1721 actually mean? Lets map it to something familiar... like the F1 25 game ratings (1-100). We use a two-point linear interpolation. It's just y = mx + c. We assume a linear correlation between my Elo and the game's rating.

Model Limitations

It is imperative to note that this is an estimation. It is not a precise reflection of the official F1 25 game logic. Asssuming a perfect linear relationship based on two data points is a massive simplification. Treat the "Implied Rating" as an illustrative abstraction. It's a point of correlation, not a definitive prediction. But it works for us ;-)