Some Post NBA Draft Analysis

Posted on June 30, 2010 by


Editor’s Note: Shawn Ryan sent some thoughts before the 2010 draft.  But time ran short and these thoughts were not posted.  Shawn, though, was nice enough to use his analysis to create the post-draft tables.  What follows are tables based on his analysis, some of his pre-draft thoughts, and some stuff the editor (DJ) threw into the post.  So this is essentially a co-authored post. Thanks again to Shawn for getting this material put together.

Shawn Ryan is a 24 year old student currently living in Austin, TX and pursuing dual degrees in Computer Science and Economics. He found the Wages of Wins at a time when he had become newly infatuated with the field of behavioral economics, and it has greatly influenced his educational goals. He has been a fan of basketball from a young age, but has tended to have different favorite teams over time, including the Suns, Cavaliers, Blazers, Hawks, and Rockets among others. He loathes NBA play-by-play commentary, and often has to resort to turning off the sound for the sake of his mental welfare. He is glad to have the opportunity to contribute to this blog, because for some reason, his fiancee tends to fall asleep at unfortunate times while he discusses his very interesting ideas about the NBA with her.

It’s that time of year again. A time when the NBA’s less fortunate fans, whether they hail from New York, or Minnesota, New Jersey or Memphis, can look to the future and just know that things are going to improve. The NBA draft is a time of infinite possibilities, a time when a single draft pick can change the face of a franchise. Fans hope to nab the next MJ, Lebron, Shaq, or Duncan. Does the 2010 NBA draft hold the next big thing, or just the next big letdown? In the following, I hope to offer some insight into that question.

Win Score and PAWS40

This insight is going to rely upon Win Score.  This is a measurement of player performance first noted in The Wages of Wins (and also in Stumbling on Wins).  It’s a simplified version of Wins Produced, built around the notion that points, rebounds, steals, field goal attempts, and turnovers have – in absolute terms – the same impact on wins.  Blocked shots, assists, free throw attempts, and personal fouls also impact wins, but the impact appears smaller.  For simplicity sake, Win Score argues that these factors have ½ the impact of points (and the factors associated with possession of the ball).

Win Score = PTS + REB + STL + ½*BLK + ½*AST – FGA – ½*FTA – TO – ½*PF

As noted in The Wages of Wins (and also in Stumbling on Wins), a player’s Win Score is impacted by position played.  Big men tend to get rebounds and not turn the ball over often. Guards tend to do the opposite.  Consequently, to compare players across positions, an adjustment has to be made for position played.  This adjustment gives us Position Adjusted Win Score (PAWS), a measure that is actually highly correlated with Win Produced.

Before we get to analyzing the NBA draft we need to adjust for minutes played.  Specifically, since a college game lasts for 40 minutes, we consider PAWS40.  This is calculated as follows: 

PAWS40 = Positions Adjusted Win Score per 40 minutes =

(Win Score per 40 minutes – Average Win Score per 40 minutes at position played) +

Average Win Score per 40 minutes for all players

As noted, to calculate PAWS40, one needs to know the average performance at each position.   From 1995 to 2009, the players selected out of college at each position posted the following average WS40:

PG: 7.40

SG: 8.40

SF: 9.95

PF: 12.59

C: 12.32

The average player across all position had a WS40 of 10.17. With these numbers in hand, we can calculate each player’s PAWS40.  For exampe, point guard John Wall – the number one pick in the draft — had a WS40 of 7.2 in 2009-10.  So his PAWS40 would be…

John Wall PAWS40 = (7.2–7.4) + 10.17 = 10.0

A mark of 10.0 is just about average for a player selected out of college from 1995 to 2009.

Since is gracious enough to calculate WS40 for almost every NBA prospect outside of high school, we are able to, with relatively little effort, calculate PAWS40 for every player selected out of college in the 2010 draft. 

Analyzing the 2010 NBA Draft

Again, an average player drafted from 1995 to 2009 posted a PAWS40 of 10.2.  Here is a list of players selected in 2010 who equaled or surpassed the 10.2 mark:

And the below average player are in the following table.

Here are some thoughts on the Draft

  • First of all, it must be emphasized (as has been noted in prior discussions of the draft), PAWS40 is correlated with future performance in the NBA.  But it is not a crystal ball.  So just because a player is above average, that does not mean he will be an above average NBA player.  And below average players with respect to PAWS40 might become above average NBA players.
  • Given the first point, it may not be the case that Damion James will eventually be the top rookie selected in the draft.
  • In fact, given the link between draft position and minutes played, even if James is a very productive player he may not lead this class in Wins Produced.  That honor may fall on DeMarcus Cousins and Evan Turner.
  • If we look at the below average players, John Wall, Greg Monroe, and Ekpe Udoh top the list.  Again, this doesn’t mean fans of the Wizards, Pistons, and Warriors will definitely be disappointed.  Still, the PAWS40 evidence is not encouraging. 

Perhaps what is said in Stumbling on Wins would help.  This is part of one of the end notes in the book:

Comparing college PAWS40 to NBA WP48 revealed that more than 80% of the drafted college players who posted a PAWS40 that was one standard deviation below the mean managed to post a career NBA WP48 that was below the mean (this was true whether you looked at players after three or five seasons). If you look at players with a PAWS40 that was one standard deviation above the mean, though, between 60% to 65% went on to post a career WP48 that was above average. These results suggest that identifying poor NBA performers with college data is easier than identifying outstanding NBA performers. Or in other words, if you play poorly in college, it’s likely that you will play poorly in the NBA. Excelling in college, though, is not a guarantee of future success.

Again, the mean PAWS40 is 10.2.  One standard deviation is 2.8.  So a player who post a PAWS40 below 7.3 have a very good chance of not being very good players.  Posting a mark above 13.0 indicates that above average performance in the NBA is more likely, but not quite a sure thing.

– Shawn Ryan & DJ