Beasley Disappoints

Posted on July 13, 2008 by

2


Last week was the Orlando Summer League.  And according to whoever determined the first and second Orlando Pro All Summer League Teams, the following players impressed:

First Team
G – Russell Westbrook, Oklahoma City
G – Courtney Lee, Orlando
F – Jeff Green, Oklahoma City
F – Michael Beasley, Miami
C – Brook Lopez, New Jersey
Second Team
G – Mario Chalmers, Miami
G – Earl Calloway, Indiana
F – Jaycee Carroll, New Jersey
F – Chris Douglas-Roberts, New Jersey
C – Marcin Gortat, Orlando

When we look at the stats, a very familiar pattern emerges.  Here are the top five per-game scorers from Orlando (minimum 100 minutes played):

1. Jeff Green (22.8 points per game)

2. Courtney Lee (20.2 points per game)

3. Brook Lopez (19.6 points per game)

4. Michael Beasley (19.6 points per game)

5. Russell Westbrook (16.5 points per game)

Apparently, the young players at the Orlando Summer League have already been introduced to a reality of NBA basketball.

If you want the most attention, you must score the most. 

When we delve into all the statistics – via Win Score and PAWS (Position Adjusted Win Score) – we see that two of the first team players didn’t really play that well.

Table One: Evaluating the Orlando Summer League Players

Table One reports each player’s PAWS48 [Position Adjusted Win Score per 48 minutes] for all the players who logged at least 100 minutes.   As one can see, Courtney Lee and Michael Beasley – two leading scorers — were actually below average performers in Orlando.  Meanwhile Andre Emmett and Kasib Powell – two players who have played very briefly in the NBA – led the Summer League in productivity.

It is important to note that our sample is only five games.  So this is not enough to say much about what these players will do in the NBA.  It is enough, though, to comment on the evaluation of Beasley’s Orlando performance.

Like Kevin Durant last summer, Beasley posted incredible numbers in college.  Such numbers led people (like me and Erich Doerr) to declare Beasley as a player likely to be an above average NBA performer.  But just like Durant, Beasley’s first foray into NBA basketball has disappointed.

Fortunately for Heat fans, there appears to be some differences between Durant’s summer league performance in 2007 and what Beasley did last week.  The following posts note that Durant couldn’t shoot or rebound last summer.

Disappointing Durant

Durant Disappoints Again

When we look at Beasley this summer we see a player who could rebound.  Beasley just had trouble getting his shots to go in and avoiding turnovers.  One suspects that this is because Beasley was treating the games last week more like a pick-up basketball game than a real NBA contest. 

Of course, as I noted last summer in discussing Durant, why Beasley played poorly is not really the story.  The important story is that Beasley did commit 3.4 turnovers per game while posting an adjusted field goal percentage of 41.4%.  And despite this performance, Beasley was told he had a “good” week.  Certainly NBA players – like everyone else – respond to incentives.  If you play poorly but are told it was good, you have very little incentive to fix the problems.

Of course, Beasley is not the only story from Orlando.  For more, I recommend the many posts on the Orlando Summer League offered by Ty at Bucks Diary.  Included in Ty’s coverage is a post on the one game played by Kevin Durant.  As Ty notes, at least for one game, the Durant we thought we were going to see after his one year at Texas made an appearance. 

– DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

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