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Using statistical regressions that controlled for a host of other variables—including the number of bidders and an engineer’s pre-auction estimate of cost as well as the third-lowest bid placed in the auction—Alan Ingraham identified two new contract specialists who presided over auctions where there was a disturbingly small difference between the winning and the second-lowest bid. Without knowing even the names of the contract specialists (the inspector general’s data referred to them by number only), we were able to point the inspector general’s office in a new direction. Alan turned the work into two chapters of his doctoral dissertation. While the results of the inspector general’s investigation are confidential, Peter was deeply appreciative and earlier this year thanked me for “helping us catch two more crooks.”
    This “magic number” story shows how Super Crunching can reveal the past. Super Crunching also can predict what you will want and what you will do. The stories of eHarmony and Harrah’s, magic numbers, and Farecast are all stories of how regressions have slipped the bounds of academia and are being used to predict all kinds of things.
    The regression formula is “plug and play”—plug in the specified attributes and,
voilà,
out pops your prediction. Of course, not all predictions are equally valuable. A river can’t rise above its source and regression predictions can’t overcome insufficient data. If your dataset is too small, no regression in the world is going to make very accurate predictions. Still, unlike intuitivists, regressions know their own limitations and can answer Ed Koch’s old campaign question, “How Am I Doing?”

CHAPTER 2
    Creating Your Own Data with the Flip of a Coin

    In 1925, Ronald Fisher, the father of modern statistics, formally proposed using random assignments to test whether particular medical interventions had some predicted effect. The first randomized trial on humans (of an early antibiotic against tuberculosis) didn’t take place until the late 1940s. But now, with the encouragement of the Food and Drug Administration, randomized tests have become the gold standard for proving whether or not medical treatments are efficacious.
    This chapter is about how business is playing catch-up. Smart businesses know that regression equations can help them make better predictions. But for the first time, we’re also starting to see businesses combine regression predictions with predictions based on their own randomized trials. Businesses are starting to go out and create their own data by flipping coins. We’ll see that randomized testing is becoming an important tool for data-driven decision making. Like the new regression studies, it’s Super Crunching to answer the bottom-line questions of what works. The poster child for the power of combining these two core Super Crunching tools is a company that made famous the question “What’s in Your Wallet?”
    Capital One, one of the nation’s largest issuers of credit cards, has been at the forefront of the Super Crunching revolution. More than 2.5 million people call CapOne each month. And they’re ready for your call.
    When you call CapOne, a recording immediately prompts you to enter your card number. Even before the service representative’s phone rings, a computer algorithm kicks in and analyzes dozens of characteristics about the account and about you, the account holder. Super Crunching sometimes lets them answer your question even before you ask it.
    CapOne found that some customers call each month just to find out their balance or to see whether their payment has arrived. The computer keeps track of who makes these calls, and routes them to an automated system that answers the phone this way: “The amount now due on your account is $164.27. If you have a billing question, press 1….” Or: “Your