So I did a thing using this sounding. My background and work is in Statistics (I have a Master's in it, and I'm a Senior Data Scientist), so the SARS Sounding Analog in the Sharppy-based soundings intrigues me. So I dug into how they are created. It turns out the analogs are ALL deterministic, meaning it takes a few variables from the sounding (like ML CAPE) and goes plus or minus 100 is a match, etc. So it's a very rough and crude technique to find analogs. Instead, I wanted to take a different approach.
So I took the entire SARS supercell tornado database off its Github and ran it through some statistics. Then I took the sounding above as a guinea pig and plugged its parameters into the model. My model uses Mahalanobis distance and k-nearest neighbors to find the 10 closest matches statistically to each sounding's variables.
For anyone curious, this returned the 10 closest soundings ---> 40% had EF2+ tornadoes and 60% were tornadic. Very strong indications of the atmosphere we may face on Saturday.