The Magical Magic and the Ariza Trade

Posted on November 21, 2007 by


One of the early surprises of the 2007-08 season is the Orlando Magic.  After 12 games the Magic have won ten times (the first team to get to double digits in wins) and currently hold the second best record in the Eastern Conference.  And by handing the Celtics their only loss of the season, there is now talk the Magic might be able to contend for a title in 2008.

How the Magic Improved

To see why all this is surprising, let’s project how many wins the Magic could have expected had each player on this team maintained his productivity from 2006-07 this season.

Table One: Projecting the Orlando Magic in 2007-08

As Table One reveals, even with the addition of Rashard Lewis, the Magic should only expect to be on pace to win about 44 games this season.  Instead, as the team’s efficiency differential of 7.35 indicates, this team is actually on pace to win about 60 games.  How do we explain this leap?

The answer can be seen in Table Two.

Table Two: The Orlando Magic After 12 Games

The improvement in this team can be traced to three players – Jameer Nelson, Dwight Howard, and Keith Bogans.  Howard has always been an above average player, so although he’s playing better than he ever has before, we are not surprised to see him produce wins.  Nelson and Bogans, though, have career marks that are below average.  After three seasons, Nelson has produced 11.1 wins and posted a WP48 [Wins Produced per 48 minutes] mark of only 0.093 (average is 0.100).  Bogans, after four seasons, had produced 4.1 wins with a 0.031 WP48.  How did each of these players improve so much?

For an answer, let’s turn to Table Three. 

Table Three: Nelson, Bogans, and Howard

This table reports the individual statistics for each player in 2006-07 and after 12 games in 2007-08.  We can see that although there are improvements in multiple areas, where all three improved is with respect to rebounds and blocked shots.  This suggests a team that is playing better defense (although we also see improvements on offense).

Now why did these players improve? Was it the addition of Rashard Lewis (who has not improved much at all)?  Is it better coaching? Is it weak competition? Is this just a fluke from a small sample of 12 games?

At this point, it’s hard to say.  But we are able to see in the data which players are responsible for the leap.  So I guess that’s something (or maybe not). 

The Trevor Ariza Trade

Apparently the leap forward is not enough for this franchise.  Now it has sent Trevor Ariza to the Lakers for Brian Cook and Maurice Evans.

Ariza’s WP48 stands at 0.153 for his career.  When we look at the 260 players who played at least 1,000 minutes last year, less than 30% posted a mark that’s this high.  In sum, Ariza is a very good player.

Cook and Evans, though, are not.  In four seasons Cook has produced 1.8 wins and posted a 0.021 WP48.  Evans, who has also played four seasons, has a career WP48 of 0.048.   In sum, the Magic traded a somewhat rare talent for two players who are decidedly below average.

The one redeeming feature of this trade is that Cook might take the minutes of Pat Garrity.  In nine seasons, Garrity has produced -14.6 wins.  Yes, he is one of the worst players to every log 10,000 minutes in an NBA career (maybe an entire post on this topic might be worthwhile someday).   

What does this trade mean for the Lakers?  That is going to have to wait for another day.  For now, though, it does look like the trade makes the West a bit stronger.  And the chances of the Magic truly contending with Boston in the East just a bit weaker.

– DJ

Our research on the NBA was summarized HERE.

The Technical Notes at 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.