If you are fan of one of the losers in the NBA (like the Nets, T-Wolves, Pistons, etc…), your thoughts are probably already turning to the NBA draft. And judging by what people are saying, it’s likely such thoughts are focusing on John Wall. As Peter May states: “(the) Kentucky guard is the first name out of everyone’s mouth.” When we look at the performance data, though, a red flag suddenly appears. Thus far, Wall’s numbers have not been exceptional.
The 2009 Draft Again
Before we get to the numbers – and what those numbers mean – let’s acquire some perspective by reviewing the 2009 NBA draft.
Table One reports what the players who were drafted last summer did in college in 2008-09. The players are evaluated in terms of Wins Score and PAWS40. As noted, these numbers are calculated as follows:
Win Score = PTS + REB + STL + ½*BLK + ½*AST – FGA – ½*FTA – TO – ½*PF
PAWS40 = Positions Adjusted Win Score per 40 minutes =
Win Score per 40 minutes – Average Win Score per 40 minutes at position played (with Average PAWS40 = 10.1)
As one can see, the most productive player listed in Table One was DeJuan Blair. And at the midpoint of the 2009-10 season, the most productive rookie was DeJuan Blair.
An Early Look at 2010
That being said, the link between college performance and NBA productivity is not perfect. So the following numbers – for the top college players identified by May – don’t necessarily “prove” (as if we are proving stuff) that a specific player will be a “good” or “bad” NBA player.
The first number listed for each player is Win Score per 40 minutes (taken from DraftExpress). The second number listed is PAWS40. Again, average PAWS40 is 10.1, so any numbers below that mark indicates that the player thus far has been below average (for a player drafted out of college).
- John Wall: 7.2 and 10.0
- Evan Turner: 15.1 and 16.8
- Wesley Johnson: 14.8 and 16.5
- DeMarcus Cousins: 20.4 and 18.0
- Derrick Favors: 14.3 and 11.9
- Cole Aldrich: 18.6 and 16.4
- Ed Davis: 16.4 and 14.0
- Al-Farouq Aminu: 13.4 and 13.6
- Solomon Alabi: 13.1 and 10.9
- Patrick Patterson: 12.7 and 10.3
- Greg Monroe: 12.3 and 9.9
- Jarvis Varnado: 17.7 and 15.3
- Xavier Henry: 7.8 and 9.5
Of these thirteen players, only three have been below average. And one of those three is Wall. So does that mean Wall shouldn’t be the first choice in the draft?
Well, not really. What these numbers represent – as noted above – is a red flag. Wall is supposed to be a dominant NBA player. Thus far, though, Wall hasn’t been a dominant college player. So before a team invests the first pick in Wall, someone has to explain why Wall has not dominated this year.
One possible explanation is that Wall is just a freshman. DeMarcus Cousins, though, is also a freshman. And Cousins is the most productive player listed above (and probably the player most responsible for Kentucky’s success this season). Additionally, Chris Paul – the most productive point guard in the NBA – was also above average as a freshman in college. In sum, it’s possible for a freshman to play well. Thus far, though, Wall’s numbers are not very impressive (although his game against Alabama last night – despite the six turnovers – was above average).
It’s important to emphasize that this doesn’t mean Wall will never be a good player. Again, college numbers are not a crystal ball. There is a correlation, though, between what we see in college and the pros. Although that relationship isn’t perfect, that relationship does suggest college numbers mean something.
And the something those college numbers appear to be saying is that teams that don’t select Wall can still find productive players. Cousins, Evan Turner, Wesley Johnson, and Cole Aldrich have all posted outstanding numbers this season. That means that if your favorite losing team fails to win the lottery, there’s still hope that your team can draft a productive player (of course, it’s also still possible that your team will make the wrong choice and draft an unproductive player).
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Our research on the NBA was summarized HERE.
Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:
Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.