Tracks

Below is a list of all of the tracks that I have developed with some metrics. For consistency, I've standardized the name of all tracks based on their locations (city/country). Tracks themselves and the FIA have been inconsistent over the years about what a track / grand prix is called.

Many tracks have changed layouts over the years. The year that a particular layout was first run is noted.

If you are interested in a detailed description of how I build tracks I wrote three posts on my blog describing my process:

1. Preparing to Build a New Track
2. Turning Research Into a New Track
3. Testing, Testing, Testing A New Track

The sheet

Key and Notes

• General Information
• Years The years that this track was run in roughly this configuration in F1.
• aka A common name that the track is known as.
• Track Length Length of the track measured in total spaces the shortest(ish) way around the track. One space converts to about 0.07 km, 0.04 miles, or 76 yards.
• Corners The number of corners (as defined in game terms).
• Scores
• Relative Score Raw Score minus the median score. Note that I calculate the median differently if the score is a Results Score, Track Score, or average of the two.
• Absolute Score If combined sample size is 20+ then this is equal to the Results Score. If I don't have any sample races, the score is equal to the Track Score. Otherwise it is an average of the Results Score and the Track Score -- weighting the Results Score score more and more the closer to 20 total samples I have.
• Results OnlyMore or less... pts scored by cars in the back and cars with 20 start speeds - pts scored by cars in the front and with 100+ start speed -- weighted if I have more samples for one of those data sets than the other. Then I divide by the standard deviation. So a Results score of 0 means that historically cars in the front and/or with 100+ start speeds score as many points on this track as cars who start in the back and/or with 20 start speeds. A -1 score means that the track has historically favored 100+ start speeds and/or starting in the front by a standard deviation... that's a significant amount.
• Sample Size Number of races where I have data related to Qualifying position and Start Speed. In cases where that number differs, the lowest value is shown here.
• Wear Score Points scored by cars with 5 or 6 wear per lap - those with 8 wear per lap... divided by the standard deviation... minus the median. In theory a 0 wear score indicates that it doesn't really favor a high wear car or a low wear car. Negative values indicate that 8 wear cars do better. Positive numbers indicate that 5 or 6 wear cars do better. But because of how this is calculated this is NOT an abdolute measure. Raw scores show that 8 wear cars will score 3+ points on average more than 5 and 6 wear cars.
• W Sample Size Number of races where I have data related to wear.

A grid showing all tracks by relative score and wear score. Background color of the track name shows the number of samples I have for all related attributes -- white = 0, black = 10+.