Baseball, Business, And Big Data

"What are you going to believe, me or your own eyes?"—Chico Marx, "Duck Soup"

What are you going to believe, me or your own eyes?—Chico Marx, Duck Soup

We are engulfed by a supernova of data. One academic study estimated, for instance, that in 2008 Americans consumed a total of 3.6 zettabytes of information (look it up: a zettabyte is a trillion gigabytes). And some futurists predict that the volume of technical data available is now doubling every few months. Getting to the right decisions while making proper use of this data requires us to corral our human biases with mathematical discipline, but it’s not always so easy to practice evidence-based management. This is just one of the topics Martha Rogers and I try to address in our new book Extreme Trust (to be released Thursday, April 26), when we discuss the rising importance of trust and trustworthiness in a highly interactive, data-rich society.

I was reminded of the difficulty of the task, however, at a party this week when by chance I met the CEO of a National League baseball team. Nice guy, tall, moneyed, very confident, and fun to talk to. Just making conversation, I asked if he’d ever read Moneyball, by Michael Lewis. "Total fiction," he sniffed, "absolute nonsense." Then he said his team had extremely smart guys on staff who relied on their extremely smart judgments to make extremely smart calls on which baseball players to hire and how much to pay for them.

But Moneyball isn’t a work of fiction. Entertaining and a great read, Lewis’s book documents the real-life story of how Billy Beane (played by Brad Pitt in the movie), manager for the Oakland A’s in 2002, combed through baseball statistics trying to find overlooked talent—players whose numbers showed they could generate results for a team but who, for one reason or another, hadn’t attracted enough attention to bid up their price.

Baseball is a profession immersed in numbers and statistics, of course. Ratios, percentages, trends—if you can count it, some baseball record-keeper somewhere has compiled it across teams, players, leagues, and seasons, and has probably already correlated it with everything from local weather patterns to birth order. So Beane looked for pitchers who got lots of ground-outs, for instance, or hitters who had high on-base percentages. He didn’t care whether someone was overweight or seemed over the hill; he only focused on the numbers. And in the end, Beane’s method helped drive the A’s to a winning record despite the fact that his team had the tiniest player payroll of any team in the majors—so small they couldn’t really afford to employ any stars.

The baseball CEO’s dismissal of the whole story, however, reminded me of something that happened more than 30 years ago, when I was an economist at a Houston-based oil company that frequently participated in the U.S. government’s regular auctions of offshore drilling rights. Every few months the government would list a number of different tracts in the Gulf of Mexico and then sell the drilling rights for each tract to the highest bidder in a sealed-bid auction. Oil company geologists rely on seismic and sonar data to map the rock structures under the sea and evaluate the likelihood that particular areas contain oil. Those tracts deemed likely to contain the most oil, of course, go for the highest prices.

Over the years our company had won some and lost some, but when my boss and I reviewed the numbers we discovered that our successful bids hadn’t generated much profit for our company. In fact, when we did win a bid, we almost never found as much oil as we thought the parcel contained (based on the geological data), and so we often didn’t earn enough money to pay for the bid we had submitted. And the bigger the potential we thought we had identified, the bigger the bust. As a result, the more bids we won, the more money we lost.

Being "economists," we soon figured out that there was a very simple, mathematical reason for this. Seismic and sonar data are not exact, and geologists’ judgments aren’t perfect, so no one can know with certainty exactly how much oil is down there under which particular rock formations. Geologists’ estimates of the volume of oil that can be produced from the same exact parcel of land do vary considerably, but our company (and most of the others) bid enthusiastically on whatever tracts our own geologists thought held the most potential, while not paying as much attention to those tracts where we estimated less chance of finding a lot of oil. But if everyone bids what they think they need to bid in order to buy a particular tract, based on their own estimate of its worth, then the highest guesses will always win the bid, and for the most part these guesses (being the highest) will turn out to be wrong.

Instead, the right bidding strategy in this kind of probabilistic situation isn’t to identify those few tracts you think have the most oil and then try to win them at auction with whatever bid is required, but to bid intentionally low on a whole lot of different parcels. Place low-ball bets on everything. You’re less likely to win any particular tract, but when you do win you’ll pay a bargain price that ensures you’ll make a profit from whatever oil is down there.

This is directly analogous to the strategy Billy Beane employed when trying to acquire baseball players. Place a lot of small, low-ball bets, rather than a few big ones, and those you win will really pay off. You won’t get the stars, but you’ll get a lot of good producers. And it’s the way businesses need to deal with the uncertainties in innovation and technological progress as well.

At last night’s party, however, when the baseball team CEO dismissed Lewis’s book with such conviction, it reminded me of what happened when my boss and I took our new offshore oil bidding plan in to the Vice President of Exploration. He looked carefully though our analysis, gave us a bright and sunny smile, and then gently but firmly told us to stick to our spreadsheets. Geologists want to discover oil, he said. What a geologist puts on his resume is what oil fields he has discovered and how big they were, not how much the company had to bid for the rights. He said he had a staff of extremely smart geologists who relied on their extremely smart judgments to make extremely smart calls regarding which oil tracts to bid on, and how much to pay for them.

There is no sure thing in baseball or business—no ironclad guarantee of success. All we can do is make our decisions as objectively as possible using whatever data we can see with our own eyes.

[Image: Flickr user Bob AuBuchon]

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