The Underrated in 2008-09

Posted on July 28, 2009 by


Who is the most “underrated” player in the NBA?  As I noted a few days ago, the answer to such a question requires two perspectives.  The first metric should capture popular perception.  The second should approximate reality.  Of course, to make such an argument you have to argue that your reality differs from popular perception (so the overrated/underrated story requires a bit of an attitude).

Measuring Popular Perception

The discussion of the “overrated” focused on three measures that appear to capture popular perception: NBA Efficiency, Game Score (John Hollinger’s simple measure), and the Player Efficiency Rating (PER or John Hollinger’s more complicated measure). 

When we consider how each of these measures is calculated it appears that we would get a different answer from each.  For example, compare the formulas for NBA Efficiency and Game Score.

NBA Efficiency = Points + Rebounds + Steals + Assists + Blocked Shots – All Missed Shots – Turnovers

Game Score = Points + 0.4*Made Field Goals – 0.7*Field Goal Attempts – 0.4*Free Throws Missed + 0.7*Offensive Rebounds + 0.3*Defensive Rebounds + Steals + 0.7*Assists + 0.7*Blocked Shots – 0.4* Personal Fouls – Turnovers

These metrics look to be different.  But when we look at the population of players from the 2008-09 regular season, we see a 0.99 correlation between a player’s NBA Efficiency and Game Score value.

PER – as the description at Basketball-Reference indicates – is more complicated than both NBA Efficiency and Game Score.  But when we compare Game Score per-minute and PER (a per-minute metric), again we see a 0.99 correlation.

In sum, each of these measures is capturing something very similar. 

And that something is scoring.  As the following posts on each measure indicates, players who score -whether efficiently or not – tend to look good according to each measure.

NBA Efficiency: Do We Overvalue Rebounds? (November 9, 2006).

PER: A Comment on the Player Efficiency Rating (November 17, 2006)

Game Score: Marvin Williams Makes a Hypothetical Deal (December 16, 2007)

Scoring, as The Wages of Wins argues, is the one factor that drives popular perception.  Consequently, metrics that are driven by scoring are also going to be good measures of how players are generally perceived.

The Preferred Measure

With the measures of popular perception once again explained, let me take a slight detour before I get to the underrated.  Let’s imagine that you wanted a measure of popular perception.  Which of these three should you choose?

The answer depends upon how you view complexity.  If you wish people to think you are clever, then complexity is considered a benefit.  In other words, the simple tends not to impress.

But in empirical research, complexity is a cost (in time and effort).  In other words, if all else is equal, a simple approach should always be preferred to a complex approach.  Or to put it another way, complexity is only good if the complexity actually gives you something. 

Given this argument, NBA Efficiency should be preferred over either Game Score or PER.  As outlined above, NBA Efficiency tells essentially the same story and it is the easiest to calculate.  My sense, though, is that PER often tends to be preferred to Game Score.  And Game Score is often preferred to NBA Efficiency. In sum, it looks like some people prefer complexity, even if that complexity isn’t giving them anything.

The Underrated

Okay, enough detours.  Let’s get to the question this post is supposed to be addressing.  Who is the most underrated player in the 2008-09?

The answer to this question will follow the same approach taken in examining the overrated.  Again, we need two reference points.  Given that this is The Wages of Wins Journal, our measure of reality (or what passes for reality in this forum) will be Wins Produced.  The ranking from this metric will be compared to three measures of popular perception: points score per game, NBA Efficiency, and PER.

Let’s start with points scored per game. 

Table One: The Underrated Scorers

Table One reports – via a comparison of points-per-game and Wins Produced – the fifteen most underrated players.  Topping the list is Jason Kidd.  He is followed by Mike Miller, Jamario Moon, Rajon Rondo, and Anderson Varejao.  Each of these players produced a significant quantity of wins, but generally not via scoring. 

Next we turn to the Wins Produced-NBA Efficiency story.

Table Two: The Underrated in NBA Efficiency

Points-per-game has a 0.89 correlation with NBA Efficiency (0.93 correlation with Game Score).  Although this is fairly high, we see some differences in the names reported in Table One and Two.  Specifically Kidd and Rondo– who were ranked towards the top of Table One – do not appear on Table Two.  Although these names disappear, Moon (who tops Table Two), Miller, Varejao, Dominic McGuire, Samuel Dalembert, Shane Battier, and Shawn Marion appear on both lists.

The final table looks at PER.  Because this is a per-minute measure, we have to compare the PER ranking to the ranking we see from WP48 [Wins Produced per 48 minutes].

Table Three: The Underrated in PER

Leading this list is McGuire.  He is followed by Miller, Moon, Battier, and Dalembert. Again, we see familiar names.  But the name at the top is again different. 

So who is the most underrated?  If we add together the difference reported from each approach the most underrated player in the NBA for 2008-09 is the latest player added to the best team from the 2008-09 regular season.  Yes, Jamario Moon (the newest player in Cleveland) tops the underrated rankings.  And here is the complete top 20.

  1. Jamario Moon
  2. Mike Miller
  3. Dominic McGuire
  4. Anderson Varejao
  5. Shane Battier
  6. Samuel Dalembert
  7. Delonte West
  8. Anthony Parker
  9. Shawn Marion
  10. James Posey
  11. Mike Conley
  12. Lamar Odom
  13. Luc Mbah a Moute
  14. Jason Kidd
  15. Ramon Sessions
  16. Jose Calderon
  17. Al Horford
  18. Luke Ridnour
  19. Rajon Rondo
  20. Mario Chalmers

Moon is not the only Cavalier to appear on the list. Varejao – who the team recently resigned – and Delonte West are also listed.  Although people tend to think of the Cavaliers as a team that begins and end with LeBron, it appears that King James did not get some underappreciated help last year.

Let me close by noting that there were some changes in the underrated rankings from 2007-08 to 2008-09.  One player that dropped out was Tyson Chandler, who had a relatively poor season this past year.  Now Chandler has been traded to the Bobcats for Emeka Okafor.  John Hollinger made the following comment on this trade (Insider access required): …Okafor is the better player. Both players consistently have been honorable mentions in my all-defense picks, but Okafor is the superior scorer. That might not be saying much — both players are somewhat limited offensively — but Okafor can score on post-ups occasionally and make short bank shots, while Chandler’s range ends at the charge circle. Over the past three seasons, Okafor has averaged nearly five more points per 40 minutes — that’s big.

If we look at the past three seasons, Chandler has a 0.230 WP48 while Okafor has a 0.222 WP48.  If we focus on just the 2006-07 and 2007-08 seasons, though, Chandler trumps Okafor 0.271 to 0.235.  Again, Chandler – primarily because of injury – had a poor season last year.  If Chandler is now healthy it’s more than possible that the Bobcats come out ahead on this deal.  At least, that appears to be true if we look past scoring.

– 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.