Carmelo Anthony Says He Needs Help

Posted on April 26, 2010 by


The notion that Carmelo Anthony is not Denver’s most productive player has been noted in this forum in the past.  And this past year this story has remained the same.  Anthony finished the 2009-10 season with a 0.112 WP48 [Wins Produced per 48 minutes] and 6.1 Wins Produced.  Of the eight players who played at least 1,000 minutes for the Nuggets this past season, five players – Chris Andersen, Chauncey Billups, Nene Hilario, Kenyon Martin, and Ty Lawson – posted higher WP48 marks.  And Hilario, Billups, Andersen, and Martin produced more wins.

With these number in mind, consider the following first few paragraphs from a story published by Mark Kizla in the Denver Post today:

After carrying an NBA franchise and the basketball dreams of a city on his shoulders for seven long years, Nuggets forward Carmelo Anthony is beginning to show the strain.

How much more of this can Melo take?

In a 117-106 loss to Utah that put the Nuggets on the brink of elimination from the playoffs, Denver looked like the same frustrating franchise it has been for most of Anthony’s career.

If Melo can’t do it, nobody can.

“I’m trying, I’m trying to beat them. I’m trying to do everything I can in my power to beat the Jazz,” Anthony said Sunday. “But, at the end of the day, I need some help. I’m not sitting here pointing fingers or nothing. As a unit, we’ve got to do this together. I can’t do this by myself.”

This article suggests that Melo has been carrying Denver throughout his career.  And again, I think the numbers suggest otherwise.

However, in this particular post-season, Anthony might have a point.  Here is how Win Score per 48 minutes [WS48 or the simplified version of WP48] has changed for each of the regular rotation players on the Nuggets as we moved from the regular season to the post-season [following numbers are playoff WS48 minus regular season WS48]:

  • Carmelo Anthony: +3.3
  • Arron Afflalo: +2.8
  • J.R. Smith: +1.1
  • Kenyon Martin: -0.8
  • Nene Hilario: -3.3
  • Chauncey Billups: -4.2
  • Ty Lawson: -5.9
  • Chris Andersen: -6.1

Most players tend to see productivity decline in the playoffs.  This is because players are playing better opponents and the pace of the game tends to slow.  Melo, though, is playing better and the Nuggets are still losing.  This tends to support the argument that Denver’s success is really not about Melo.  His supporting cast is really the key to this team’s success.  And even with Melo playing much better, Denver is still losing because the primary producers of wins on this team are just not playing well.

And why is this important?  I picked Denver in the TrueHoop challenge.  This was the hardest series to call, and I essentially was guessing.  After Utah lost Andrei Kirilenko I felt very good about my choice.  But then the key players on the Nuggets stopped producing. Consequently my drive to repeat is being threatened.  One would think that this alone would inspire Denver’s players to try harder.  I sense, though, that my plight is not being considered.

Let me close by noting that fans of Utah – a group that surrounds me in Cedar City – are very happy.  Repeating the above analysis for Utah reveals that the happiness I see is primarily due to the play of Paul Millsap [6.2 increase in WS48] and Deron Williams [3.1 increase in WS48].  If these two players keep playing well – and the aforementioned Denver players keep struggling – I might continue to be the only person in Utah who is not enjoying the NBA playoffs. 

– DJ

The WoW Journal Comments Policy

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.