The Bottom 10% and One Big Reason Why the Celtics are Having Problems

Posted on April 21, 2009 by


The NBA season ended last week and now we have (thanks to all the data needed to measure each player’s Wins Produced (and WP48, Win Score, etc…) for the 2008-09 season.  With data set in hand, there are numerous stories to be told.   While most people start with the best (i.e. MVP, All-NBA, Rookie of the Year, etc…), I thought I would start with a story that is often overlooked.  Specifically, who are the least productive players – in terms of Wins Produced — in the NBA?

Before we get to the list, let’s define what it means to be “unproductive”.  A productive player will tend to shoot efficiently, grab rebounds, gets steals, and avoids turnovers.  So an unproductive player is one that tends to shoot inefficiently, fails to rebound and get steals, and is prone to commit turnovers. 

When we pair productive players with unproductive performers we see that the latter can minimize the success of the former.  In other words, a productive player can get a rebound or steal, only to see the unproductive miss the shot or commit a turnover. Or the productive player can miss a shot, only to see the unproductive fail to get the rebound.  In sum, just as a team cannot succeed without productive players, it can also find failure when it employs too many unproductive performers.

The Least Productive Lead the Way

So who are the unproductive performers? During this past season 445 players were employed by NBA teams.  So the bottom 10% would consist of 45 names.  And topping this list – as Table One reveals – is Al Thornton of the LA Clippers.  Consequently Thornton has been named the Least Productive Player – or LP2 – for 2008-09.

Table One: The Bottom 10% in 2008-09

Obviously Thornton and the Clippers struggled this year.  But as we scan down the list we can that the Clippers’ problems are not all about Thornton.  In addition to Thornton, the LA Clippers employed Ricky Davis (ranked 7th), Tim Thomas (ranked 11th and a Clipper for part of the year), and Steve Novak (ranked 31st).  So although the Clippers also employed Marcus Camby (the 13th most productive player in the game), the abundance of unproductive players did much to limit this team’s success.

A similar story could be told about the Toronto Raptors.  Toronto employs both Jose Calderon (20th most productive player) and Chris Bosh (26th most productive player).  But the Raptors also have Andrea Bargnani (5th least productive), Jason Kapono (6th least productive), Roko Ukic (27th least productive), and Jake Voskuhl (28th least productive).  When Toronto fans seek to understand why this season was so unsuccessful, this quartet – that played more than 25% of the team’s minutes – should certainly come to mind.

Three other teams also employed at least three players listed in Table One.  The Wizards received -4.47 Wins Produced from Nick Young (10th least productive), Darius Songaila (23rd least productive), and DeShawn Stevenson (33rd least productive).  Meanwhile the Minnesota Timberwolves were led to 58 losses by Bobby Brown (3rd least productive and a Minnesota employee for part of the year), Jason Collins (12th least productive), Brian Cardinal (24th least productive), and Sebastian Telfair (25th least productive).

Bringing Down Boston

And then there are the Celtics.  The Boston Celtics won 62 games this past season.  When we look at Wins Produced we see the team received 48.3 Wins Produced from Rajon Rondo (6th most productive), Paul Pierce (29th most productive), Kevin Garnett (30th most productive), and Ray Allen (35th most productive).  But the team also received -4.44 Wins Produced from Glen Davis (9th least productive), Brian Scalabrine (20th least productive), and Stephon Marbury (41st least productive).

Unfortunately for the Celtics, KG is probably lost for the entire playoffs.  And perhaps more dismaying to Boston fans is that the team has turned to Glen Davis to take many of Garnett’s minutes.   Had Davis played all the minutes played by Garnett this year, Davis would have seen his Wins Produced fall from -1.98 to -4.12.  Plus the Celtics would have lost the 10.16 Wins Produced from Garnett.  The outcome of these changes is that the Celtics would have seen their Wins Produced fall from 61.06 to 48.76 (assuming the per-minute performance from Davis did not change).  In other words, moving from Garnett to Davis takes the Celtics from a title contender to a team that will struggle to defeat the Bulls.

We only heard that KG was lost for the playoffs the day the TrueHoop picks were due.  And my schedule last Thursday prevented me from doing much thinking about this before I had to submit my choices. As a consequence, I thought the Celtics would still be good enough to defeat the Bulls in five games.  Although this is still possible, it seems unlikely.  Yes, the Celtics did win on Monday night.  And yes, Davis actually played well.  But given what Davis did in 2008-09, it doesn’t seem possible for Davis and the Celtics to take the next three from Chicago. 

And this means that once Chicago wins another game in this series, my rooting interest in this series moves from the Celtics to the Bulls.  Everyone in the Smackdown picked Boston, but only Neal Paine and I took Boston in five games.  So to stay even with the other competitors, once “Boston in Five” can’t happen anymore I need Chicago to take the series (and then everyone loses the points).

If Boston keeps playing Glen Davis, it seems possible that the defending champions will indeed lose in the first round.  And the legend of Derrick Rose – which I will address in a future post – will certainly grow.

– 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.