A couple of extra points about my piece in this week’s New Yorker on “Wages of Wins.”
For those of you who haven’t read the article, it was a review of a book by three economists—David Berri, Martin Schmidt, and Stacey Brook—who have come up with an algorithm for assessing the value of professional basketball players. Simply put, they rank players according to what they call a Win Score—which is the number of wins that player alone can be said to have been responsible for in a given season.
Here’s the amazon link to the book:
The author’s website is: www.wagesofwins.com
I’ve noticed, in reading reactions to the book around the blogosphere, a certain residual skepticism, particularly among hard-core basketball fans. Someone wrote in to point out, for instance, that Shawn Marion’s Win Score this past season was higher than Steve Nash’s, when common sense would suggest that the team would suffer far more from the loss of Nash than Marion. I think that's right. Nash is more ultimately more valuable to the Suns than anyone else.
Basketball is tricky. No statistical formula can adequately measure the series of intangible factors that are so critical to a team’s success: a player’s impact on his teammates, for example, or attitude, or willingness to play hurt, or grace under pressure or—most important of all—how well a player plays defense. Nash’s particular, largely unquantifiable; genius is that he manages to make everyone around him much better. As Bill Simmons (world’s greatest sportswriter) points out in his column today, Tim Thomas was traded to the Suns this season after nine years of disappointment, and all of a sudden he played like a star. Is that a conincidence? I don't think so.
Similarly, the Wages of Wins algorithm tells us that over the course of his career Ray Allen has been “worth” nearly as much to the teams he has played for as Kobe Bryant. Does that mean Allen is as good as Bryant? Of course not. Bryant is one of the greatest on-the-ball defenders of his generation and Allen is, well (let’s be nice here) not. Perhaps the best part of Kobe's game doesn't—and probably can't—show up in any kind of statistical analysis.
But the Wages of Wins guys aren’t arguing that their formulas are the only and best way to rate players. They are making a more sophisticated—and limited—claim: for those aspects of basketball performance that are quantifiable (steals, turnovers, rebounds, shots made and missed, free throws etc) are the existing statistical measures we use to rate players any good? And if not, is there a better way to quantify the quantifiable?
To the first question, “Wages of Wins” argues—convincingly—no. For instance, they show that the correlation between a team’s payroll and a team’s performance, in the NBA, is surprisingly weak. What that tells us is that the people charged with evaluating and rewarding ability and performance in the NBA do a lousy job. In particular, they argue, traditional talent evaluation over-rates the importance of points scored, and under-rates the importance of turnovers, rebounds and scoring percentage. Wages of Wins also obliterates the so-called NBA Efficiency rating, which is the official algorithm used by the league and many basketball experts to rank the statistical performance of players. The Efficiency rating, they argue, makes the same error. It dramatically over-rewards players who take lots and lots of shots.
Okay: part two. Is the Wages of Wins algorithm an improvement over the things like the NBA Efficiency system? To make the case for their system, the authors “fit” their algorithm to the real world. For the 2003-04 season, they add up the number of wins predicted by their algorithm for every player on every team, and compare that number to the team’s actual win total. Their average error? 1.67 wins. In other words, if you give them the statistics for every player on a given team, they can tell you how many wins that team got that season, with a margin of error under two wins. That’s pretty good.
Here’s what I think the real value of the Wages of Wins system is, though. It gives us a tool to see those instances where our intuitive ratings of players may be particularly inaccurate. In my New Yorker piece, I focused on how the algorithm tells us that Allen Iverson isn’t nearly the player we think he is. But here’s a more interesting finding. The best player, by this measure, hands down, over the past five years has been Kevin Garnett. No one else comes close. I had the authors update their numbers for this season, and Garnett is again number one (with Jason Kidd second, Shawn Marion third and LeBron James fourth). Why wasn’t Garnett’s name mentioned in the MVP voting? I know it’s fashionable these days to rag on Garnett for not making his teammates better or some such. But as David Berri told me, what the Wins Score numbers say is that every year Garnett gets better and better, and every day the quality of the players that Kevin McHale surrounds him with gets worse and worse. (Can you say Ricky Davis?)
Just for fun, here are some Wins Scores numbers for this season. Here are the players who’s Wins Score rankings differ the most from their NBA Efficiency rankings—that is, the players who’s “true” value diverges greatest from conventional wisdom, according to the Wins Score system.
Most under-rated, in order:
- Josh Childress
- Tyson Chandler
- Eddie Jones
- Chris Duhon
- Mike Miller
- Delonte West
- Antonio Daniels
- Shane Battier
- Luther Head
- Drew Gooden
Here are the ten most over-rated.
- Al Harrington
- Carmelo Anthony
- Zach Randolph
- Richard Hamilton
- Chris Webber
- Nenad Krstic
- Allen Iverson
- Mike Bibby
- Antwawn Jamison
- Ricky Davis
Now argue with that list all you want. Factor in intangibles. Make projections. Move some people up and down. But once you’ve read the book, I promise you won’t be able to dismiss it.