u/EntrepreneurNo204

Hey everyone so with the upcoming NBA draft I decided to create a draft model that regresses NCAA college stats to an NBA metric (RAPM).

Essentially what I did was:

for every player from 2008-2021, I took a bunch of NCAA stats as their features, engineered few more and standardized everything as much as I could
used their rookie window (1-4 years) NBA RAPM as the target feature
Split 2008-2018 data into train (n=422) and 2019-2021 into test (n=124)
Ran ElasticNet and XGBoost (hyperparameter tuned with CV) on this dataset and both gave me R2 of just ~0.07

This is probably a longshot as most people on here likely don't follow the NBA like that or know what RAPM is, but if you had to guess, would you say that this is just the reality of these models, or am I just doing something wrong?

These are the 19 features I used: r2P, r3P, rFT, AST/TOV, USG%, PTS/100, 2PA/100, 3PA/100, AST%, FTR, ORB%, DRB%, Stops/100, STL%, BLK%, PFR, Team Barthag Rating, Team Strength of Schedule, Draft Age

Created an NBA draft model. R2 is too low?