FanPost

How analytics grade draft prospects (part 1)


Since the Sixers' season is all about adding assets, developing players, or tanking, I figured the best thing to post on is the NBA draft as tanking and trades have been pretty well covered. This will be a two part post, and in the first part I'll talk a bit about how stat guys grade college players. Second post will be how I expect stat guys to grade the 2014 prospects that we will pick from and who I personally like and dislike. But first, let's discuss the 2013 draft and why both Noel and MCW were guys that stat guys loved.

To begin with the first thing when analyzing college players impact on the NBA is that not every skill translates the same way to the NBA. Some skills seem like they should translate directly (free throw shooting percentage) since the job seems identical at college and the NBA. Some skills seem like they might not translate as well (scoring for example) since players may shoot a lot more in college where they are option 1 and not in the pros where they might be the fourth option on the team. Though this information is from 2009 and thus outdated and I'm sure top front offices have much better information than this, a great article on basketball statistics helps answer this question.

http://basketball-statistics.com/howdoncaastatisticstranslatetothenba.html

What it basically says is stats in college correlate to the NBA as follows:

  • Blocks and assists have the highest correlation to the NBA
  • Rebounds, 3 point shooting, and free throw shooting are highly correlated.
  • Number of 3 point attempts, steals and turnovers are solidly related
  • Anything related to 2pt shooting, overall scoring, or fouling or getting fouled has the lowest relation to the NBA.

So from a high level a "stat guy" would usually be strong in things like assists, blocks, or rebounds and a non-stat guy would be the one who is a good scorer in college, specifically from inside the arc or getting to the free throw line. I also think shooting or reducing turnovers are things that can improve over time whereas things like height generally do not, and thus it may explain why some stats (like FT%) that should be exactly correlated aren't as direct of a translation. One additional statement though is that these stats don't really take into account everything on defense that a college person might bring to the NBA. I'll admit it is tough to find facts on this, but from what I've been able to gather steals (especially for perimeter players) and blocks (especially for interior players) tend to correlate well to overall defensive ratings. Likewise, height has a premium itself not reflected above, as although there is a benefit to rebounding from height (a great article by Tom Haberstroh before he joined ESPN shows that each inch you have over an opponent increases your odds of getting a rebound by 1.65%) there are other benefits such as altering shots or passes that aren't reflected in the box score but show up under advanced defensive statistics.

http://hardwoodparoxysm.com/archive/2010/05/21/nba-hd-inching-for-a-rebound/


Putting all of this together and you end up with a number of interesting observations. When drafting for the NBA you want to focus on blocks, steals, and height from a defensive standpoint as blocks and height translate almost directly and steals seem to be indicative of positive factors like overall quickness. Likewise stat guys tend to focus on assists and rebounds, and from a scoring perspective shooting 3s is the most important individual factor with free throwing shooting percentage a close second. What stat guys don't worry as much about are things like 2 point shooting, overall scoring, turnovers or fouls, as though those categories matter they seem to be less indicative of success. I think the best way to show this analysis is by example, and thus here are the pace adjusted stats per 40 minutes of two players drafted in the first round in the 2013 draft (from sports-reference.com).

FG% 2P% 3P% FT% TRB AST STL BLK TOV PF PTS Height
Player A 0.393 0.436 0.294 0.694 5.6 8.3 3.1 0.6 3.9 2.6 13.5 6'6"
Player B 0.463 0.506 0.384 0.801 3.6 7.5 1.8 0.6 2.5 2.1 21.1 6'


What you can clearly see is that Player B was the better player in college. He was the more efficient shooter, had a better assist to turnover ratio and scored a lot more points. Player A was the better rebounder and had more steals, but other than height he seems to have few advantages. However, if we look to translate these stats based upon the information above we can see that Player A's skills likely translate to the NBA, whereas Player B advantages are less likely to translate based upon historical information. This isn't to say one is good and one is bad (they are the two leaders in the ROY right now - hope you know who) but the point is that though MCW has clearly surpassed expectations this year a closer look at his stats would reveal someone that had some skills likely to translate. I picked Burke as comparison as they were the only two highly rated true point guards (frankly Burke wasn't bad in a lot of categories... he was more well-rounded. Using Len or even Oladipo as a comparison (not as high in analytic world.) wouldn't have been as effective due to difference of positions they played in college. In case you are wondering about Noel he is a poor free throw shooter but great in most every other area especially blocks, rebounding, and steals. I'll talk about him more in part 2 but suffice to say he should be either the Number 1 or Number 2 pick in a redraft today with MCW the only player I see giving him a run for his money.

In the end there is no magic analytical formula that everyone uses. Each front office or individual has their own factors they use to measure players. But usually there is a common thread (like walks in baseball) and when looking at college players some stats translate well (blocks, rebounds, etc.) and some don't (scoring, 2pt FG %). Hope you gentlemen found this useful and if anyone finds defensive correlation stats from college to NBA I'd love to see them.

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