Capital Ideas - Summer 2013 - page 9

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Capital Ideas |
Summer 2013
7
T
he best-selling book
Moneyball
by Michael Lewis
tells the story of how in 2002 the Oakland Ath-
letics, a Major League Baseball team with a budget
of about one-third of the New York Yankees payroll,
revamped its lineup and eventually went on one of
baseball history’s longest winning streaks. Rigorous
statistical analysis convinced the Oakland A’s that on-
base and slugging percentages—qualities that weren’t
highly valued by other teams—were actually better in-
dicators of players’ offensive abilities. This new way of
assessing player performance helped the Oakland A’s
find the most undervalued players in the market.
If baseball can do it, what about ice hockey? After
a labor dispute and lockout, National Hockey League
(NHL) team owners might use statistics to make sure
they’re getting the best players for the money. Research
by Chicago Booth Professors
Robert Brandon Gramacy
and
Matt Taddy,
with Shane Jensen of Wharton, sug-
gests a useful metric.
While baseball has been transformed by statisti-
cians, hockey remains less affected. This is partly be-
cause baseball generates more data than hockey does.
Moreover in hockey, it’s far more difficult to isolate
individual performance.
Hockey’s most popular measure of individual per-
formance, beyond goals and assists, is plus-minus val-
ue: players on the ice get a “plus” for every goal scored
by their team and a “minus” for every goal scored by
the opposition. A player could theoretically score lots
of goals but still have a negative plus-minus value if
the opposition scored more.
While its simple formulation is appealing, the plus-
minus statistic has important flaws as a means of deter-
mining a player’s worth, according to the researchers.
For one, a player’s plus-minus score depends partly
on the performance of his teammates and opponents,
which makes evaluating a player’s performance based
on his own abilities more challenging.
For example, most people have never heard of Rob
Brown, who in his first two seasons in the NHL, with
the Pittsburgh Penguins, scored 174 points and had a
+35 plus-minus rating. Brown spent those two years
playing on a line with Mario Lemieux, one of hockey’s
all-time greats, and to whom Brown owed thanks for
his impressive statistics. When he was traded two years
later, to the Lemieux-less Hartford Whalers (now the
Carolina Hurricanes), Brown scored only 73 points in
two seasons, and his plus-minus dipped to -21, under-
scoring how plus-minus ratings can fail to accurately
capture individual performance.
Through new techniques developed in the study,
Gramacy, Jensen, and Taddy were able to come up
with a more precise measure of performance—one
that can isolate each player’s unique contribution to a
goal. Using a type of statistical analysis called regular-
ized logistic regression, which can estimate the credit
or blame that should be apportioned to each player ev-
ery time a goal is scored, they drew conclusions about
player performance that were markedly different from
traditional plus-minus figures. When the authors ap-
plied this new performance measure to data from four
regular NHL seasons (2007–11), they found that far
fewer players stood out from their team’s average per-
formance, and they were able to identify overvalued
and undervalued players.
The Pittsburgh Penguins’ Sidney Crosby is consid-
ered by many to be the best player in the NHL. But
using the more precise measure shows that he made
a much smaller contribution to goals than his plus-
minus rating suggests. The Detroit Red Wings’ Pavel
Datsyuk was actually the league’s best player, by the
new metric.
Vanessa Sumo
Robert Brandon Gramacy, Shane Jensen, and Matt
Taddy, “Estimating Player Contribution in Hockey
with Regularized Logistic Regression,” Journal of
Quantitative Analysis in Sports, March 2013.
How to find the best
hockey players for the money
STATISTICS
Photo: Getty Images
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