Who is not MVP in 2010?

The NBA has essentially reached the midpoint.  As of last night, only five teams – Boston, Charlotte, Chicago, Golden State, and Milwaukee — have yet to play 41 games.  As each team reaches the 41 game mark I have measured the productivity – via Wins Produced – of each player these teams employ.  After this weekend I will analyze the remaining five teams, and then I will offer a series of posts analyzing what the NBA looks like at the midpoint of the 2009-10 season.

As we wait, though, let me offer a brief comment on the MVP race.  And here it is:

For those who think Carmelo Anthony is the most productive players in the NBA… well, I ain’t seeing it.  Carmelo leads the NBA in scoring, but after 41 games Melo has only produced 4.5 wins and posted a 0.160 WP48.  Yes, Melo is above average.  But I don’t think he’s an MVP.

What about Kevin Durant?  He has clearly improved from what we saw his rookie season (and yes, I still think he improved during his second season).  Currently he is third in the NBA in scoring and after 41 games he has posted 8.2 Wins Produced.  And although his WP48 stands at 0.240, he’s also not the most productive player in the game.

And then there is Kobe.  He’s fourth in the NBA in scoring but has only produced 6.0 wins after 41 games.  And although producing about twelve wins in a season is very good for a shooting guard, it isn’t going to lead the NBA in 2009-10.

So who is the most productive player thus far in 2009-10?  Well, I will discuss that and other topics… starting on Sunday.

- DJ

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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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