Closed durandom closed 1 year ago
I've only just stumbled upon this project but thought to offer a couple of things here. I wouldn't include all laps. You should only use say the fastest 25% of laps. Slow laps will contain bad inputs and times which will be 'outliers'. Outliers can heavily impact mean values, which could result in the mean being moved away from where the 'centre' of the desired data actually is.
But overall agree with intent to harness power of the crowd here. Interesting stuff.
@adenoz thanks for the input. Do you want to take a crack at this?
https://github.com/b4mad/racing/blob/main/notebooks/exploratory_data_analysis.ipynb is a notebook to get started with accessing the data.
You can also access the influx backend via https://telemetry.b4mad.racing/ ping me on discord https://discord.com/invite/MaUQuSGSbJ for access credentials
Hey thanks. I'll take a look. Can't promise when that'll be as I have a bunch on for at least the next few weeks.
we have an initial implemantion at https://github.com/b4mad/racing/blob/main/notebooks/fast_lap_analysis-v2.ipynb
for the pitcrew to work, we need track information in CVS files, like https://github.com/b4mad/racing/blob/main/pitcrew/Ferrari%20488%20GT3%20Evo%202020-oschersleben%20gp.csv
These are currently built manually 😢
Let's use some data science to create them automatically, maybe like this