Post a Comment On: Frankly Speaking

A fantastic piece written by Malcolm Gladwell about a new book entitled The Wages of Win: Taking Measure of the Many Myths in Modern Sport. Absolutely fantastic, and dare I say, a must-read:



A couple of extra points about my piece in this week’s New Yorker on “Wages of
Wins.”
www.newyorker.com/critics/books/?060529crbo_books1.
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:
1. Josh Childress
2. Tyson Chandler
3. Eddie Jones
4. Chris Duhon
5. Mike Miller
6. Delonte West
7. Antonio Daniels
8. Shane Battier
9. Luther Head
10. Drew Gooden
Here are the ten most over-rated.
1. Al Harrington
2. Carmelo Anthony
3. Zach Randolph
4. Richard Hamilton
5. Chris Webber
6. Nenad Krstic
7. Allen Iverson
8. Mike Bibby
9. Antwawn Jamison
10. 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.