As we inch closer to the NBA draft, you are going to hear about a ton of prospects, and you don’t have time to research all of them.
You will, however, want to learn as much as you can about these guys before draft night arrives on June 23. While a glance at their counting stats like points and rebounds per game will tell you how productive they were last season, it doesn’t actually provide all that much information on their style of play.
Players will receive comparisons based on their size and athleticism, and we used data to help contextualize testing at the NBA Draft Combine. Now, I’d like to also use sourced data from Synergy Sports Tech to better understand how a prospect plays the game.
Is he someone who creates his own shot in isolation? Is he a catch-and-shoot guy who likes to spot up on the perimeter? Is he a big man who mostly lives in the post? This is incredibly useful information for player evaluation and more statistically accurate player comparisons!
Of course, how these players were used was largely dependent on their coaches. Also, just because someone was used in a certain way last year does not mean it’s how they will be used forever.
Prospects develop new skills all the time. But before they make the jump to the next level, the data can tell us a bit more about the prior experience that someone will bring to an offense.
I should make it clear that I very much don’t have the type of brain to create these equations by myself. I’m a firm believer in using math in sports, which is why I find this information so useful. However, none of this story would be possible without people who are much smarter than I am.
This was an idea derived from writing by Jeff Siegel (who now works as a salary cap specialist for Klutch Sports Group) and Todd Whitehead (who is now working for Synergy Sports). You can read more from Whitehead about the subject here, here, and here.
The formulas were created by The BBall Index, and you can learn more about them here. Definitions for each category of player were included in each section below.
(Note: Data was tracked on a possession-by-possession basis for the NCAA, G League, Overtime Elite, and international prospects. There was no data to use for Canadian prospects Shaedon Sharpe and Leonard Miller.)