Everything Bad Is Good for You (2 page)

This book is an old-fashioned work of persuasion that ultimately aims to convince you of one thing: that popular culture has, on average, grown more complex and intellectually challenging over the past thirty years. Where most commentators assume a race to the bottom and a dumbing down—“an increasingly infantilized society,” in George Will's words—I see a progressive story: mass culture growing more sophisticated, demanding more cognitive engagement with each passing year. Think of it as a kind of positive brainwashing: the popular media steadily, but almost imperceptibly, making our minds sharper, as we soak in entertainment usually dismissed as so much lowbrow fluff. I call this upward trend the Sleeper Curve, after the classic sequence from Woody Allen's mock sci-fi film, where a team of scientists from 2173 are astounded that twentieth-century society failed to grasp the nutritional merits of cream pies and hot fudge.

I hope for many of you the argument here will resonate with a feeling you've had in the past, even if you may have suppressed it at the time—a feeling that the popular culture isn't locked in a spiral dive of deteriorating standards. Next time you hear someone complaining about violent TV mobsters, or accidental onscreen nudity, or the inanity of reality programming, or the dull stares of the Nintendo addicts, you should think of the Sleeper Curve rising steadily beneath all that superficial chaos. The sky is not falling. In many ways, the weather has never been better. It just takes a new kind of barometer to tell the difference.

T
HE
S
LEEPER
C
URVE

E
VERY CHILDHOOD HAS
its talismans, the sacred objects that look innocuous enough to the outside world, but that trigger an onslaught of vivid memories when the grown child confronts them. For me, it's a sheaf of xeroxed numbers that my father brought home from his law firm when I was nine. These pages didn't seem, at first glance, like the sort of thing that would send a grade-schooler into rapture. From a distance you might have guessed that they were payroll reports, until you got close enough to notice that the names were familiar ones, even famous: Catfish Hunter, Pete Rose, Vida Blue. Baseball names, stranded in a sea of random numbers.

Those pages my dad brought home were part of a game, though it was a game unlike any I had ever played. It was a baseball simulation called APBA, short for American Professional Baseball Association. APBA was a game of dice and data. A company in Lancaster, Pennsylvania, had analyzed the preceding season's statistics and created a collection of cards, one for each player who had played more than a dozen games that year. The cards contained a cryptic grid of digits that captured numerically each player's aptitudes on the baseball diamond: the sluggers and the strikeout prone, the control artists and the speed demons. In the simplest sense, APBA was a way of playing baseball with cards, or at least pretending to be a baseball
manager
: you'd pick out a lineup, decide on your starting pitchers, choose when to bunt and when to steal.

APBA sounds entertaining enough at that level of generality—what kid wouldn't want to manage a sports team?—but actually playing the game was a more complicated affair. On the simplest level, the game followed this basic sequence: you picked your players, decided on a strategy, rolled a few dice, and then consulted a “lookup chart” to figure out what happened—a strikeout, or a home run, a grounder to third.

But it was never quite that simple with APBA. You could play against a human opponent, or manage both teams yourself, and the decisions made for the opposing team transformed the variables in subtle but crucial ways. At the beginning of each game—and anytime you made a substitution—you had to add up all the fielding ratings for each player in your lineup. Certain performance results would change if your team was unusually adept with the glove, while teams that were less talented defensively would generate more errors. There were completely different charts depending on the number of runners on base: if you had a man on third, you consulted the “Runner on Third” chart. Certain performance numbers came with different results, depending on the quality of the pitcher: if you were facing a “grade A” pitcher, according to the data on his card, you'd get a strikeout, while a “grade C” pitcher would generate a single to right field. And that was just scratching the surface of the game's complexity. Here's the full entry for “Pitching” on the main “Bases Empty” chart:

Got that? They might as well be the tax form instructions you'd happily pay an accountant to decipher. Reading these words now, I have to slow myself down just to follow the syntax, but my ten-year-old self had so thoroughly internalized this arcana that I played hundreds of APBA games without having to consult the fine print.
An 11 in the second column on the batter's card? Obviously,
obviously
that means ignore the normal grade changes for the pitcher. It'd be crazy not to!

The creators of APBA devised such an elaborate system for understandable reasons: they were pushing the limits of the dice-and-cards genre to accommodate the statistical complexity of baseball. This mathematical intricacy was not limited to baseball simulations, of course. Comparable games existed for most popular sports: basketball sims that let you call a zone defense or toss a last-minute three-point Hail Mary before the clock ran out; boxing games that let you replay Ali/Foreman without the rope-a-dope strategy. British football fans played games like Soccerboss and Wembley that let you manage entire franchises, trading players and maintaining the financial health of the virtual organization. A host of dice-based military simulations re-created historical battles or entire world wars with painstaking fidelity.

Perhaps most famously, players of Dungeons & Dragons and its many imitators built elaborate fantasy narratives—all by rolling twenty-sided dice and consulting bewildering charts that accounted for a staggering number of variables. The three primary manuals for playing the game were more than five hundred pages long, with hundreds of lookup charts that players consulted as though they were reading from scripture. (By comparison, consulting the APBA charts was like reading the back of a cereal box.) Here's the
Player's Handbook
describing the process by which a sample character is created:

And that's merely defining the basic faculties for a character. Once you released your Dwarven fighter into the world, the calculations involved in determining the effects of his actions—attacking a specific creature with a specific weapon under specific circumstances with a specific squad of comrades fighting alongside you—would leave most kids weeping if you put the same charts on a math quiz.

Which gets to the ultimate question of why a ten-year-old found any of this
fun.
For me, the embarrassing truth of the matter is that I did ultimately grow frustrated with my baseball simulation, but not for the reasons you might expect. It wasn't that arcane language wore me down, or that I grew tired of switching columns on the Bases Empty chart, or that I decided that six hours was too long to spend alone in my room on a Saturday afternoon in July.

No, I moved on from APBA because it wasn't realistic enough.

My list of complaints grew as my experience with APBA deepened. Playing hundreds of simulated games revealed the blind spots and strange skews of the simulation. APBA neglected the importance of whether your players were left-handed or right-handed, crucial to the strategy of baseball. The fielding talents of individual players were largely ignored. The vital decision to throw different kinds of pitches—sliders and curveballs and sinkers—was entirely absent. The game took no notice of
where
the games were being played: you couldn't simulate the vulnerable left-field fence in Fenway Park, so tempting to right-handed hitters, or the swirling winds of San Francisco's old Candlestick Park. And while APBA included historic teams, there was no way to factor in historical changes in the game when playing teams from different eras against each other.

And so over the next three years, I embarked on a long journey through the surprisingly populated world of dice-baseball simulations, ordering them from ads printed in the back of the
Sporting News
and Street and Smith's annual baseball guide. I dabbled with Strat-o-Matic, the most popular of the baseball sims; I sampled Statis Pro Baseball from Avalon Hill, maker of the then-popular Diplomacy board game; I toyed with one title called Time Travel baseball that specialized in drafting fantasy teams from a pool of historic players. I lost several months to a game called Extra Innings that bypassed cards and boards altogether; it didn't even come packaged in a box—just an oversized envelope stuffed with pages and pages of data. You rolled six separate dice to complete a play, sometimes consulting five or six separate pages to determine what had happened.

Eventually, like some kind of crazed addict searching for an ever-purer high, I found myself designing my own simulations, building entire games from scratch. I borrowed a twenty-sided die from my Dungeons & Dragons set—the math was far easier to do with twenty sides than it was with six. I scrawled out my play charts on yellow legal pads, and translated the last season's statistics into my own home-brewed player cards. For some people, I suppose, thinking of youthful baseball games conjures up the smell of leather gloves and fresh-cut grass. For me, what comes to mind is the statistical purity of the twenty-sided die.

This story, I freely admit, used to have a self-congratulatory moral to it. As a grownup, I would tell new friends about my fifth-grade days building elaborate simulations in my room, and on the surface I'd make a joke about how uncool I was back then, huddled alone with my twenty-sided dice while the other kids roamed outside playing capture the flag or, God forbid,
real
baseball. But the latent message of my story was clear: I was some kind of statistical prodigy, building simulated worlds out of legal pads and probability charts.

But I no longer think that my experience was all that unusual. I suspect millions of people from my generation probably have comparable stories to tell: if not of sports simulations then of Dungeons & Dragons, or the geopolitical strategy of games like Diplomacy, a kind of chess superimposed onto actual history. More important, in the quarter century that has passed since I first began exploring those xeroxed APBA pages, what once felt like a maverick obsession has become a thoroughly mainstream pursuit.

This book is, ultimately, the story of how the kind of thinking that I was doing on my bedroom floor became an everyday component of mass entertainment. It's the story of how systems analysis, probability theory, pattern recognition, and—amazingly enough—old-fashioned
patience
became indispensable tools for anyone trying to make sense of modern pop culture. Because the truth is my solitary obsession with modeling complex simulations is now ordinary behavior for most consumers of digital age entertainment. This kind of education is not happening in classrooms or museums; it's happening in living rooms and basements, on PCs and television screens. This is the Sleeper Curve: The most debased forms of mass diversion—video games and violent television dramas and juvenile sitcoms—turn out to be nutritional after all. For decades, we've worked under the assumption that mass culture follows a steadily declining path toward lowest-common-denominator standards, presumably because the “masses” want dumb, simple pleasures and big media companies want to give the masses what they want. But in fact, the exact opposite is happening: the culture is getting more intellectually demanding, not less.

Most of the time, criticism that takes pop culture seriously involves performing some kind of symbolic analysis, decoding the work to demonstrate the way it represents some other aspect of society. You can see this symbolic approach at work in academic cultural studies programs analyzing the ways in which pop forms expressed the struggle of various disenfranchised groups: gays and lesbians, people of color, women, the third world. You can see it at work in the “zeitgeist” criticism featured in media sections of newspapers and newsweeklies, where the critic establishes a symbolic relationship between the work and some spirit of the age: yuppie self-indulgence, say, or post-9/11 anxiety.

The approach followed in this book is more systemic than symbolic, more about causal relationships than metaphors. It is closer, in a sense, to physics than to poetry. My argument for the existence of the Sleeper Curve comes out of an assumption that the landscape of popular culture involves the clash of competing forces: the neurological appetites of the brain, the economics of the culture industry, changing technological platforms. The specific ways in which those forces collide play a determining role in the type of popular culture we ultimately consume. The work of the critic, in this instance, is to diagram those forces, not decode them.

Sometimes, for the sake of argument, I find it helpful to imagine culture as a kind of man-made weather system. Float a mass of warm, humid air over cold ocean water, and you'll create an environment in which fog will thrive. The fog doesn't appear because it somehow symbolically reenacts the clash of warm air and cool water. Fog arrives instead as an emergent effect of that particular system and its internal dynamics. The same goes with popular culture: certain kinds of environments encourage cognitive complexity; others discourage complexity. The cultural object—the film or the video game—is not a metaphor for that system; it's more like an output or a result.