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undermine their authority, and Selig agreed that the owners would
merely sit in the room, observing the eminences deliberate.

It didn't matter. In luly 2000, the panel did pretty much exactly
what Bud Selig hoped it would do: conclude that poor teams didn't
stand a chance, that their hopelessness was Bad for Baseball, and
that a way must be found to minimize the distinction between
rich and poor teams. George Will, the conservative columnist,
was, oddly enough, the most outspoken proponent of baseball
socialism. One dramatic fact Will often used to incite alarm was



THE SCIENCE OF WINNING AN UNFAIR GAME 121

that the ratio of the payrolls of the seven richest and seven poor-
est teams in baseball was 4:1, while in pro basketball it was 1.75:1
and in pro football 1.5:1. Baseball was the major American sport in
which money bought success, he said, and that was a crime
against the game. When fans of the Brewers and the Royals and the
Devil Rays figured out that their teams existed only so that the
New York Yankees might routinely pummel them, they would
abandon the sport altogether. At stake was nothing less than the
future of professional baseball.

There was something to be said for these arguments but there
was also something to be said against them, and, according to two
people who watched the proceedings, only one commissioner was
willing to say it: Paul Volcker. Volcker was also the only commis-
sioner with a financial background. To the growing annoyance of
the others, he kept asking two provocative questions:

1. If poor teams were in such dire financial condition, why did rich
guys keep paying higher prices to buy them?

2. If poor teams had no hope, how did the Oakland A's, with the
second lowest payroll in all of baseball, win so many games?

The owners didn't have a good answer to the first question, but
to answer the second they dragged in Billy Beane to explain him-
self. The odd thing was that the previous season, 1999, the A's had
finished 87-75 and missed the play-offs. Still, they had improved
dramatically from 1998, Billy's first year on the job, when they'd
gone 74-88. And they were looking even stronger in 2000. Volcker
smelled a rat. If results in pro baseball were so clearly determined
by financial resources, how could there be even a single excep-
tion? How could a poor team improve so dramatically? Paul
DePodesta wrote Billy Beane's presentation and Billy flew off to
New York to explain to Volcker why he was a fluke. He was happy
to do it. He hadn't the slightest interest in stopping the Blue Rib-
bon Panel from concluding that his life was unfair. He'd be



122



MONEYBflLL



delighted to see the cost of players constrained, or, even better, the
Yankees made to give him some of their money. When he got up
before the panel, Billy flashed a slide up on the overhead projector.
It read:



MAJOR LEAGUE

'Movie about the hapless Cleveland Indians

In order to assemble a losing team, the owner distributes a
list of players to be invited to spring training. The baseball
executives say that most of these players are way past their
prime. Fans see the list in the paper and remark, "I've never
even heard ot halt these guys."

I Our situation closclv resembles the movie.

I .

When It suited his purposes Billv could throw the best pity party
this side of the Last Supper. He told the Blue Ribbon Panel that the
Oakland A's inability to afford famous stars meant that no matter
how well the team performed, the fans stayed away — which was
the opposite of the truth. All the A's marketing studies showed
that the main thing tans cared about was winning. Win with
nobodies and the fans showed up, and the nobodies became stars;
lose with stars and the fans stayed home, and the stars became
nobodies. Assembling nobodies into a ruthlessly efficient machine
for winning baseball games, and watching them become stars, was
one of the pleasures of running a poor baseball team.

Billy also told the Blue Ribbon Panel that his inability to pay
the going rate for baseball players meant that his success was
likely to be ephemeral. It might have been what they wanted to
hear but it wasn't what he believed. What he believed was what
Paul Volcker seemed to suspect, that the market for baseball play-
ers was so inefficient, and the general grasp of sound baseball
strategy so weak, that superior management could still run circles
around taller piles of cash. He then went out and created more evi-



THE SCIENCE OF WINNING AN UNFAIR GAME 123

dence in support of his belief. Having won 87 games in 1999, the
Oakland A's went on to win 91 games in 2000, and an astonishing
102 games in 2001, and made the play-offs both years.

They weren't getting worse, they were getting better. The rap-
idly expanding difference between the size of everyone else's
money pile and Oakland's had no apparent effect. Each year the
Oakland A's seemed more the financial underdog and each year
they won more games. Maybe they were just lucky. Or maybe they
knew something other people didn't. Maybe they were, as they
privately thought, becoming more efficient. When, in 2001, for the
second year in a row, they lost to the Yankees in the fifth and
deciding game of the play-offs, the Oakland front office was cer-
tain that theirs had been the better team and that it was the Yan-
kees who had gotten lucky — and that the Yankees front office
knew it. And that some fraction of the $120 million the Yankees
had paid Jason Giambi after the 2001 play-offs to lure him away
from the Oakland A's was to prevent him from ever again playing
for the Oakland A's.

At any rate, by the beginning of the 2002 season, the Oakland
A's, by winning so much with so little, had become something of
an embarrassment to Bud Selig and, by extension. Major League
Baseball. "An aberration" is what the baseball commissioner, and
the people who worked for him, called the team, and when you
asked them what they meant by that nebulous word, they said,
though not for attribution, "They've been lucky." This was the
year the luck of the A's was meant to run out. The relative size of
the team's payroll had shrunk yet again. The difference between
the Yankees' and A's opening day payrolls had ballooned from $62
million in 1999 to $90 million in 2002. The Blue Ribbon Panel's
nightmare scenario for poor teams had become a reality for the
2002 Oakland A's. They had lost to free agency— and thus, to
richer teams — three of their proven stars: Jason Isringhausen,
Johnny Damon, and Giambi.

To a financial determinist like Bud Selig, the wonder must have



124 MONEYBflLL

been that they hadn't simply given up. Of course, no one in pro
sports ever admits to quitting. But it was perfectly possible to
abandon all hope of winning and at the same time show up every
day for work to collect a paycheck. Professional sports had a word
for this: "rebuilding." That's what half a dozen big league teams
did more or less all the time. The Kansas City Royals had been
rebuilding for the past four or five years. Bud Selig's Brewers had
been taking a dive for at least a decade. The A's didn't do this, for
the simple reason that they actually believed they were going to
keep on winning — perhaps not so manv games as they had in
2001, but enough to get themselves hack to the play-otfs.

Before the 2002 season, Paul DePodesta had reduced the com-
ing six months to a math problem. He ludged how many wins it
would take to make the play-offs: 9.S. He then calculated how
many more runs the Oakland A's would need to score than they
allowed to win 95 games: b^.S. (The idea that there was a stable
relationship between season run totals and season wins was
another lamesean discoverv.' Then, using the A's plavers' past per-
formance as a guide, he made reasoned arguments about how
many runs they would actually score and allow. If they didn't suf-
fer an abnormally large number of injuries, he said, the team
would score between 800 and 820 runs and give up between 6.S0
and 670 runs.* From that he predicted the team would win
between 93 and 97 games and probablv wind up in the play-offs.
"There aren't a lot of teams that win ninety-hve games and don't
make it to the play-offs," he said. "If we win ninety-five games
and don't make the play-offs, we're fine with that."

The 2001 Oakland A's had won 102 regular season games. The
2002 Oakland As entered the season without the three players
widely regarded by the market to be among their best and the
expected result was a net loss of seven wins. How could that be?
The only way to understand the math was to look a bit more

* They wound up scoring 800 and allowing 653.



THE SCIENCE OF WINNING AN UNFAIR GAME 125

closely at what, exactly, the team lost, or believed they lost, when
other, richer teams hired away each of the three stars.

The first, and easiest, player to understand was their old flame-
throwing closer, Jason Isringhausen. When Billy Beane had traded
for him in the middle of the 1999 season, Isringhausen was pitch-
ing in the minor leagues with the New York Mets. To get him and
a more expensive pitcher named Greg McMichael and the money
to pay McMichael's salary, all Billy Beane had given up was his
own established closer, Billy Taylor. Taylor, who ceased to be an
effective pitcher more or less immediately upon joining the Mets,
Billy Beane had himself plucked from the minor leagues for a few
thousand dollars a few years earlier.

The central insight that led him both to turn minor league
nobodies into successful big league closers and to refuse to pay
them the many millions a year they demanded once they became
free agents was that it was more efficient to create a closer than to
buy one. Established closers were systematically overpriced, in
large part because of the statistic by which closers were judged in
the marketplace: "saves." The very word made the guy who
achieved them sound vitally important. But the situation typi-
cally described by the save — the bases empty in the ninth inning
with the team leading — was clearly far less critical than a lot of
other situations pitchers faced. The closer's statistic did not have
the power of language; it was just a number. You could take a
slightly above average pitcher and drop him into the closer's role,
let him accumulate some gaudy number of saves, and then sell
him off. You could, in essence, buy a stock, pump it up with false
publicity, and sell it off for much more than you'd paid for it. Billy
Beane had already done it twice, and assumed he could do so over
and over again.

Jason Isringhausen's departure wasn't a loss to the Oakland A's
but a happy consequence of a money machine known as "Selling
the Closer." In return for losing Isringhausen to the St. Louis Car-
dinals, the A's had received two new assets: the Cardinals' first-



126 MONEYBflLL

round draft pick, along with a first-round compensation pick. The
former they'd used to draft Benjamin Fritz, a pitcher they judged
to have a hrighter and cheaper tuture than Isringhauseu; the latter,
to acquire Jeremy Brown.

The Blue Rihhon Commission had asked the wrong question.
The question wasn't whether a hasehall team could keep its stars
even after they had finished with their six years of indentured
servitude and became free agents. The question was: how did a
baseball team find stars in the tirst place, and could it tind new-
ones to replace the old ones it lost' How fungible were baseball
players' The short answer was: a lot more fungible than the peo-
ple who ran baseball teams believed.

Finding pitchers who could become successful closers wasn't
all that difficult. To fill the hole at the back of his bullpen Billy
had traded to the Toronto Blue lavs a minor league third baseman,
Eric Hinske, for Billv Koch, another crude fireballer. He knew that
Hinske was very good — he'd wind up being voted 2(){)2 Rookie of
the Year in the American League — but the Oakland A's already
had an even better third baseman, Eric Chavez. Plus, Billy knew
that, barring some disaster, Koch, too, would gain a lot of value as
an asset. Koch would get his saves and be perceived by other
teams to be a much more critical piece of a successful team than
he actuallv was, whereupon the A's would trade him for some-
thing cheaper, younger, and possibly even better.

The loss of johnny Damon, the A's former center fielder, pre-
sented a different sort of problem. When Damon signed with
Boston, the A's took the Red Sox's first-round pick (to select Nick
Swisher) plus a compensation pick. But Damon left two glaring
holes: on defense in center field, on offense in the leadoff spot. Of
the two the offense was the easiest to understand, and dismiss.
When fans watched Damon, they saw the sort of thrilling leadoff
hitter that a team simply had to have if it wanted to be competi-
tive. When the A's front office watched Damon, they saw some-
thing else: an imperfect understanding of where runs come from.



THE SCIENCE OF WINNING AN UNFAIR GAME 127

Paul DePodesta had been hired by Billy Beane before the 1999
season, but well before that he had studied the question of why
teams win. Not long after he'd graduated from Harvard, in the
mid-nineties, he'd plugged the statistics of every baseball team
from the twentieth century into an equation and tested which of
them correlated most closely with winning percentage. He'd
found only two, both offensive statistics, inextricably linked to
baseball success: on-base percentage and slugging percentage.
Everything else was far less important.

Not long after he arrived in Oakland, Paul asked himself a ques-
tion: what was the relative importance of on-base and slugging
percentage? His answer began with a thought experiment: if a
team had an on-base percentage of 1.000 (referred to as "a thou-
sand") — that is, every hitter got on base — how many runs would it
score?* An infinite number of runs, since the team would never
make an out. If a team had a slugging percentage of 1.000 — mean-
ing, it gained a base for each hitter that came to the plate — how
many runs would it score? That depended on how it was achieved,
but it would typically be a lot less than an infinite number. A team
might send four hitters to the plate in an inning, for instance. The
first man hits a home run, the next three make outs. Four plate
appearances have produced four total bases and thus a slugging
percentage of 1 .000 and yet have scored only one run in the inning.

* These "percentages" are designed to drive anyone who thinks twice about
them mad. It's one thing to give 1 10 percent for the team, but it is another to get
on base 1,000 percent of the time. On-base "percentage" is actually on-base "per
thousand." A batter who gets on base four out of ten times has an on-base "per-
centage" of four hundred (.400). Slugging "percentage" is even more mind-bending,
as it is actually "per four thousand." A perfect slugging "percentage" — achieved by
hitting a home run every time — is four thousand: four bases for every plate appear-
ance. But for practical purposes, on-base and slugging are assumed to be measured
on identical scales. At any rate, the majority of big league players have on-base per-
centages between three hundred (.300) and four hundred (.400) and slugging per-
centages between three hundred and fifty (.350) and five hundred and fifty (.550).



128 MONEYBflLL

Baseball fans and announcers were just then getting around to
the Jamesean obsession with on-base and slugging percentages.
The game, slowly, was turning its attention to the new statistic,
OPS (on base plus slugging). OPS was the simple addition ot on-
base and slugging percentages. Crude as it was, it was a much bet-
ter indicator than any other offensive statistic oi the number of
runs a team would score. Simply adding the two statistics together,
however, implied thev were of equal importance. If the goal was
to raise a team's OPS, an extra percentage point ot on-base was as
good as an extra percentage point of slu,g,ging.

Before his thought experiment Paul had felt uneasv with this
crude assumption; now he saw that the assumption was absurd.
An extra point of on-base percentage was clearlv more valuable
than an extra point of slugging percentage — but by how much? He
proceeded to tinker with his own version of Bill lames's "Runs
Created" formula. When he was tinished, he had a model tor pre-
dicting run production that was more accurate than anv he knew
of. In his model an extra point ot on-base percentage was worth
three times an extra point of slugging percentage.

Paul's argument was radical even by sabermetric standards. Bill
lames and others had stressed the importance of on-base percent-
age, but even they didn't think it was worth tbree times as much
as slugging. Most offensive models assumed that an extra point of
on-base percentage was worth, at most, one and a half times an
extra point of slugging percentage. In major league baseball itself,
where on-base percentage was not nearly so highly valued as it
was by sabermetncians, Paul's argument was practically heresy.

Paul walked across the hall from his office and laid out his argu-
ment to Billy Beane, who thought it was the best argument he had
heard in a long time. Heresy was good: heresy meant opportunity.
A player's ability to get on base — especially when he got on base
in unspectacular ways — tended to be dramatically underpriced in
relation to other abilities. Never mind fielding skills and foot
speed. The ability to get on base — to avoid making outs — was



THE SCIENCE OF WINNING AN UNFAIR GAME 129

underpriced compared to the ability to hit with power. The one
attribute most critical to the success of a baseball team was an
attribute they could afford to buy. At that moment, what had been
a far more than ordinary interest in a player's ability to get on base
became, for the Oakland A's front office, an obsession.

To most of baseball Johnny Damon, on offense, was an extraor-
dinarily valuable leadoff hitter with a gift for stealing bases. To
Billy Beane and Paul DePodesta, Damon was a delightful human
being, a pleasure to have around, but an easily replaceable offen-
sive player. His on-base percentage in 2001 had been .324, or
roughly 10 points below the league average. True, he stole some
bases, but stealing bases involved taking a risk the Oakland front
office did not trust even Johnny Damon to take. The math of the
matter changed with the situation, but, broadly speaking, an
attempted steal had to succeed about 70 percent of the time before
it contributed positively to run totals.

The offense Damon had provided the 2001 Oakland A's was
fairly easy to replace; Damon's defense was not. The question was
how to measure what the Oakland A's lost when Terrence Long,
and not Johnny Damon, played center field. The short answer was
that they couldn't, not precisely. But they could get closer than
most to an accurate answer — or thought that they could. Some-
thing had happened since Bill James first complained about the
meaninglessness of fielding statistics. That something was new
information, and a new way of thinking about an old problem.
Oddly, the impulse to do this thinking had arisen on Wall Street.

In the early 1980S, the U.S. financial markets underwent an
astonishing transformation. A combination of computing power
and intellectual progress led to the creation of whole new markets
in financial futures and options. Options and futures were really
just fragments of stocks and bonds, but the fragments soon
became so arcane and inexplicable that Wall Street created a sin-



130 MONEYBflLL

gle word to describe them all: "derivatives." In one big way these
new securities differed from traditional stocks and bonds: they
had a certain, precisely quantifiable, value. It was impossible for
anyone to say what a simple stock or bond should be worth. Their
value was a matter of financial opinion,- they were worth whatever
the market said they were worth. Rut fragments of a stock or
bond, when you glued them back together, must be worth exactly
what the stock or bond was worth. If thcv were worth more or less
than the original article, the market was said to be "metticient,"
and a trader could make a fortune trading the fragments against
the original.

For the better part of a decade there were huge, virtually risk-
less profits to be made by people who figured this out. The sort of
people who quickly grasped the math of the matter were not typ-
ical traders. They were highlv trained mathematicians and statis-
ticians and scientists who had abandoned whatever thev were
doing at Harvard or .Stantord (ir MIT to make a killing on Wall
Street. The fantastic sums ot money hauled in by the sophisti-
cated traders transformed the culture on Wall Street, and made
quantitative analysis, as opposed to gut feel, the respectable way
to go about making bets in the market. The chief economic con-
sequence of the creation of derivative securities was to price risk
more accurately, and distribute it more efficiently, than ever
before in the long, risk-obsessed history of financial man. The
chief social consequence was to hammer into the minds of a gen-
eration of extremely ambitious people a new connection between
"inefficiency" and "opportunity," and to reinforce an older one,
between "brains" and "money."

Ken Mauriello and lack Armbruster had been part of that gen-
eration. Ken analyzed the value of derivative securities, and Jack
traded them, for one of the more profitable Chicago trading firms.
Their firm priced financial risk as finely as it had ever been priced.
"In the late 1980s Kenny started looking at taking the same
approach to Major League baseball players," said Armbruster.



THE SCIENCE OF WINNING AN UNFAIR GAME 131

"Looking at the places where the stats don't tell the whole truth —
or even lie about the situation." Mauriello and Armbruster's goal
was to value the events that occurred on a baseball field more
accurately than they ever had been valued. In 1994, they stopped
analyzing derivatives and formed a company to analyze baseball
players, called AVM Systems.

Ken Mauriello had seen a connection between the new complex
financial markets and baseball: "the inefficiency caused by sloppy
data." As Bill James had shown, baseball data conflated luck and
skill, and simply ignored a lot of what happened during a baseball
game. With two outs and a runner on second base a pitcher makes
a great pitch: the batter hits a bloop into left field that would have
been caught had the left fielder not been Albert Belle. The shrewd
runner at second base, knowing that Albert Belle is slow not just
to the ball but also to the plate, beats the throw home. In the
record books the batter was credited with having succeeded, the
pitcher with having failed, and the left fielder and the runner with
having been present on the scene. This was a grotesque failure of
justice. The pitcher and runner deserved to have their accounts
credited, the batter and left fielder to have theirs debited (the for-
mer should have popped out; the latter somehow had avoided
committing an "error" and at the same time put runs on the board
for the other team).

There was hardly a play in baseball that, to be precisely valued,
didn't need to be adjusted for the players involved, or the ballpark
in which it occurred. What AVM's system really wanted to know
was: in every event that occurs on a baseball field, how — and how
much — should the players involved be held responsible, and there-
fore debited and credited? Answer the question and you could
answer many others. For example: How many doubles does Albert
Belle need to hit to make up for the fly balls he doesn't catch?

How to account for a player's performances was obvious: runs.
Runs were the money of baseball, the common denominator of
everything that occurred on a field. How much each tiny event on



•smKaoaaB



132 MONEYBflLL

a baseball field was worth was a more complicated issue. AVM
dealt with it by collecting ten years ot data from major league
baseball games, of every ball that was put into play. Every event
that followed a ball being put into play was compared bv the svs-
tem to what had typically happened during the previous ten years.
"No matter what happens in a baseball game," said Armbruster,
"it has happened thousands of times before." The performance of
the players involved was always judged against the average.

A lot oi this was no different trom what Hill James and Dick
Cramer had set out to do ten years earlier, when thev created


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Online LibraryMichael (Michael M.) LewisMoneyball : the art of winning an unfair game → online text (page 11 of 24)