Game One Thoughts

Posted on June 7, 2008 by


When I analyze an entire season I go to all the effort to calculate Wins Produced.

When I want to evaluate players in a single game, though, I turn to Win Score and PAWS (Position Adjusted Win Score).

Wins Score and PAWS (again)

Just to review… Win Score – the simple version of Wins Produced – is calculated as follows:

Win Score = PTS + REB + STL + ½*BLK + ½*AST – FGA – ½*FTA – TO – ½*PF

PAWS is necessary because player performance depends on position played.  As noted in The Wages of Wins, Centers and power forwards tend to get rebounds and not commit many turnovers.  Guards tend to be the opposite.  When we look at players from 1991-92 to 2007-08 we see that Win Score per 48 minutes varies across position as follows:

Centers: 10.99

Power Forwards: 10.45

Small Forwards: 7.44

Shooting Guards: 6.20

Point Guards: 6.42

To get PAWS48 (PAWS per 48 minutes) you simply subtract from each player’s Win Score per 48 minutes the corresponding position average. 

Game One Best (and Worst)

PAWS48 and PAWS was calculated for each player in Game One of the 2008 NBA playoffs.

Table One: Analyzing Game One of the 2008 NBA Finals

The players listed in Table One were evaluated according to the position where they played the majority of their minutes.  Looking at the Boston Celtics, we see the leader in PAWS was Paul Pierce.  As those who watched the game will remember, Pierce nearly died in the third quarter.  But he was resurrected after just a few minutes and managed to return to lead Boston to victory.

Pierce was not the only Celtic to play well.  Leon Powe, Ray Allen, Kevin Garnett, and Rajon Rondo were all above average. James Posey, though, was the second least productive player in the game.

Posey would have been the least productive player, but Kobe Bryant grabbed that honor.  Although Kobe played quite badly (a fact I think everyone – including Kobe – acknowledges), he was not the sole reason the Lakers struggled.  With the exception of Derek Fisher, Vladimir Radmanovic, and Pau Gasol, every player on the Lakers was below average with respect to PAWS.

True Hoop Stat-Geek Smackdown Thoughts

In 2007 I participated in Henry Abbott’s True Hoop Stat-Geek Smackdown, placing a respectable third.  This year I was not able to participate (for reasons I will explain in a few weeks).  I did, though, take the whole “geek” thing one step further.   Whereas the geeks Henry assembled used numbers to forecast the winners in the playoffs, I used what I know about the methods used by the geeks to forecast the winner of the smackdown.  Yes, I forecasted the “top geek” (and what that makes me I do not wish to consider). And my choice – Justin Kubatko – did indeed win the contest.

Looking at last year’s contest, it appeared that Kubatko and I were using essentially the same approach. In forecasting the winner of each series we considered each team’s efficiency differential.  Kubatko, though, took one extra step.  He also considered home court advantage.  And with this extra step he was able to win in 2007.  Given that I think his method is best, I fully expected Kubatko to win again in 2008.

It’s interesting to note that Kubatko has picked the Lakers to prevail in the finals.  If we consider regular season efficiency differential and home court advantage, the Celtics are the obvious choice.  The Lakers acquisition of Pau Gasol, though, presents a problem. If you only consider what the Lakers did with Gasol in the line-up, you could argue the Lakers are better than the Celtics. At least, that’s what Kubatko is arguing.

Although I understand the argument, I am still going to stick with the Celtics.  I would note, though, that the teams are pretty close.  And I am not sure the numbers can call a series between teams where there is so little difference.   In essence, I think calling this series involves little more than guessing.  Of course, although I understand this point, I still hope I guess right.

– 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.