Hello everyone,

I've been looking at performance balance in various ways for several years. Professionally I've recently come across JASP and I've now put the LFS world records as of 10th October 2024 and several league races into a multiple linear regression. My goal is to determine the performance factors and perhaps derive a performance index as a way to apply BOP and evaluate the theoretical performance of car mods. The attached preliminary results are a work in progress.

The starting point for the analysis is the LFS World Records. I copied the tables from lfsworld.net on 10 October 2024 and converted the lap times from the typical format to seconds. Knowing the length of the track, the lap times were converted to speeds in metres per second. I also extended the dataset to include selected league races from that year.
Then I collected information about the cars. At first I only took the information available on lfs.net, but it turned out that the car datasets on the content page did not fully match the datasets of the mods on the files page, so I took the data from in-game. This also allowed me to get downforce values for the cars, although I only used the base setup values at 40 m/s in the linear regression.

At first the results were rather unimpressive with large residuals. By including the circuits as factors, the results improved greatly and I am hopeful that I am on the right track. Other covariates such as torque did not produce significant results, so they were not considered at this stage. I also decided to use the natural logarithms of the speeds as the dependent variable to obtain the coefficients "as factors".

The regression model currently has the following dependent variable:

• log(Speed)
  

the following covariates:

• Mass
  
• Power
  
• Weight distribution (front)
  
• Engine size
  
• Downforce Lift
  
• Downforce Drag
  

and the following factors:

• Track (not shown below)
  
• Category
  
• Drivetrain
  
• Tyres
  
• Engine layout
  
• Transmission
  

The input data and results pdf are included in the attached archive. The resulting coefficients must be transformed using the exponential function. The results are based on a formula car driven on BL1 with a paddle shift gearbox. The engine layout is a V-engine and slick tyres are used. All coefficients shown are deviations from this standard car.

Looking at the standardised coefficients, we can already see that power has the greatest influence on the car's performance. However, it is closely followed by the downforce (lift) of the car.

I am at a very early stage in this analysis. I hope to get some meaningful results from it. If not, I will at least gain experience with JASP. I'm looking forward to your ideas and insights. Maybe you will find weaknesses I can work on. I'd like to improve this approach in the future.

Best regards!

Are you trying to plot which property makes a car fast?
I am not sure what I am looking at here. Many numbers for sure.
Some random thoughts:

Races often already have some BOP applied, that makes it hard to compare.
Even if no BOP is used, the track, distance, mandatory pit stops etc might be chosen in a way to make it balanced. Typical example for TBO class might be that FXO is fastest but drivers eventually need to slow down to save tires.

Another idea:
Maybe try to figure out something about the difficulty/technicality of car/track combos by plotting a histogram of all uploaded times.
For example for XFG@Kyoto Oval I would expect that all the top times are very close to each other. On the other hand something like Fo8 at South City might have bigger gaps.
There might be a large plateau at XFG@Bl1 of newbies using default setups or other such things.

The ratio between "average speed" and "theoretical car top speed" (aka how much of a lap is at full throttle or high speed) could be interesting, too.
We know that Kyoto Oval is the fastest track, but which track is second fastest and which is the slowest?
(Also for different cars)


Mildly related:
Do you know of a way to get all WR times and their upload date?
It might be interesting to see how WR times have improved over time, how big the improvements were, which WR was standing longest until it got beaten etc.