Anyone who has analyzed historical NBA draft picks should know that translating success to the NBA is never a sure thing. No matter how good the scout, GM, or draft model, there is still a high level of uncertainty involved in predicting NBA success.
My goal is to highlight this uncertainty. By putting less emphasis on a single number, and more emphasis on a probabilistic range of outcomes, our evaluation of players can be more representative of the roll of the dice that NBA teams face.
If you want to learn more about the machine learning behind these projections - check out last year's post as well as this Twitter thread which went over model changes for this year. You might also be interested in this Twitter thread where I shared real-time insight on players as they were drafted.
Follow me on Twitter if you find this interesting!
Select a player to see a player draft profile including:
Statistical Profile - visualize the strengths, weaknesses, and growth trajectory of individual players over their college careers
Playing Style Comps - find the most similar player seasons compared to the potential draftees most recent season, which can keep you honest on a players expected outcome
