idol season ten | week 25 |1026 words
The Waffle House Index
It was a weird job, but someone had to do it, and yes—that someone was me. I'm Cal Estingham, one of the foremost experts in the dark art of Predictive Mathematics.
Now, I know what you're probably thinking: So, how can I get rich off of that? The answer is, you can't. You can make a living at it, sure, but that's not the same. And it's not something that can be taught.
Heck, I can barely explain it.
Analysts crunch data. They look at statistical models and trends, and try to guess the projected year-over-year rate of inflation, or whether the yen will rise against U.S. currency—or Alice Cooper will tour again. Sometimes they get creative, like looking at the length of women's skirts to gauge the health of the economy. Though that's more reactive than predictive, but you get the idea.
Compared to my work, it's almost ordinary.
Predictive Mathematics deals in long-term observation and strange side-effects. For instance, you might use the curl of feathers at the base of a mallard's tail to determine the wetness of the coming year. I tracked slang sequences and the average price of beer as an indicator of upcoming cycles in crime. I often consulted the BBI—the Brooks Brothers Index—as a factor in other calculations. The sales' volumes of men's suits frequently reflects the degree of local and national economic stability.
We're not talking about examining sheep's entrails or anything like that, but you know—it wasn't exactly straightforward.
Most people entered this field late, but I began experimenting with it when I was still a child. I studied the habits of caterpillars, the patterns of shadows, the commercials on television, all with the idea of figuring out whether we would have ice cream after dinner, or whether I could stay up late for extra trick-or-treating on Halloween. None of that information was remotely useful for predicting the answers to those questions, but finding out what didn't work was part of the learning process too.
Later, I graduated to better data-groupings. I weighed the mean and median scores on Mrs. Frankel's history tests against her rate of caffeine consumption, to determine my chances of getting an "A" if I brought her Hershey bars across the grading period. I watched the rise and fall of the canned tuna pile in the pantry, using those critical Tuna Casserole Event Indicators to decide when to eat dinner at a friend's house. In college, I always checked the dorm RA's major and monitored the health of the dean's marriage. Combining those with the number of weeks plus or minus midterms gave me a sense of whether a random room inspection would get me and my girlfriend in trouble. Though some of that might have been pure instinct…
Nobody else in my family was especially good at math, let alone the type of calculations I did. My cousin, Billy, used to think he had the gift for it, but he only ever used it to bet on the ponies. Given that he had about the same lack of success as everyone else, it looks like he was wrong.
My own income was spotty. It was all freelance work, though most of my jobs were for the government. I just didn't know whether it would be feast or famine from one month to the next. I tried creating an algorithm to predict that, but it was a nightmare of recursion that was ultimately useless.
I was often hired to do general observation and prediction, with specs on "areas of interest." For instance, I spent hours—weeks—collating data on loan foreclosures, angry letters to the editor, and alcohol sales, and then applied weather forecasts and televised sports schedules as qualifiers. It was all groundwork to predict if or when the Revolution was coming. (Seriously—that job came up at least once a year).
Or maybe I'd watch CCTV footage of main streets in multiple cities, compiling the MTT, Mean Time to Tailfins, as a factor in Medicare budgeting. Sometimes, it was hard to tell whether what I was doing was genius or just nuts, but it was all about the results…
All of this went on for years, with me gathering my weird data and funneling it sideways to come to my uncanny conclusions. I'd spend months inspecting waterfowl or counting the blossom yield on bush beans, and meanwhile other people were getting married and starting families. I didn't mind—my work served the needs of humanity and the appeased the pitiless gods of quantification.
But then one day, I had this sort of epiphany (eh, that's probably too strong a word for it). I pulled my head out of my data and realized, "Jesus, I'm looking at duck butts for a living."
Like I wanted that on my tombstone.
I was fifty years old, and all I had to show for it was money in the bank and a history of successful predictions. And when you really look at it, being right isn't all it's cracked up to be.
"What do you want to do with your life?" my friend Bernard asked.
"Something important. Or no—maybe not important. I don't know. I've built my whole identity around numbers. What else could I do?"
"Well, you've always had an active imagination," Bernard said. He was a newspaper editor with no aptitude for math, and it was an old joke between us. But I began to think about it in a different way.
I write children's books now—bizarre fantasies with dancing squirrels, or aliens from a banana planet trying to find the perfect spaceship. I'm not raking in the big bucks, but I love the work, and the books sell well enough to buy food and pay the rent. I might even meet a nice woman and have a family of my own someday, who knows? But in the meantime, my stories bring happiness to the children who read them.
And if those children should happen to learn something along the way that helps them avoid becoming pills or an unfortunate accident statistic, well…
So much the better.
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