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:
 Preparing to Build a New Track
 Turning Research Into a New Track
 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+.
