What was happening to capitalism should have happened to baseball: the technical man with his analytical magic should have risen to prominence in baseball management, just as he was rising to prominence on, say, Wall Street.
-Michael Lewis, Moneyball
America’s national pastime is a numbers game. Batting averages and strikeouts are the raw material for the grand narratives of the sport, separating timeless hall-of-famers from forgotten journeymen. Since Henry Chadwick popularized the box score way back in 1859, the game has been inseparable from its statistics. For fans, the history of baseball is a rhapsody of elegant numbers, the simplicity of which smooths out the game’s rougher edges: its century-long labor conflicts, its fraught race relations, the greedy self-dealing of owners and ball clubs. The attraction to baseball’s numbers points to an even deeper obsession, a truly American pastime: the fantasy of optimizing away every flaw, identifying and eliminating every inefficiency with statistics, number-crunching our way to success.
In July 2003, Michael Lewis’s Moneyball: The Art of Winning an Unfair Game hit bookshelves across the country to significant critical and popular acclaim. A bond-trader-turned-journalist, Lewis had already made a name for himself with his 1989 autobiographical bestseller Liar’s Poker: Rising through the Wreckage on Wall Street. With Moneyball, Lewis shifted his attention to a different financial market, one not often considered under such terms: Major League Baseball.
Lewis detailed the story of general manager Billy Beane’s successful attempt to turn the 2002 Oakland Athletics into a World Series contender through the adoption of data-driven analytics in order to identify and utilize undervalued players. Example approaches included giving more weight to metrics better correlated with team wins (such as on-base percentage) and drafting players out of college, rather than high school, where statistics were less reliable due to poorer competition. With an improbable lineup identified using Beane’s method, the Athletics transformed their prospects on a shoestring payroll, winning 103 games on the way to an American League West divisional title.
An immediate success, Moneyball would continue to grow in visibility over the next twenty years. The 2011 film adaptation of the book starring Brad Pitt, Jonah Hill, Philip Seymour Hoffman, and Robin Wright grossed $110 million worldwide at the box office and received six Academy Award nominations, including for Best Picture. By 2016, the book had sold 1.7 million copies. Today, the words “moneyball” and “sabermetrics” — a term derived from SABR, or the Society for American Baseball Research — are now nearly household terms. Suffice it to say that Moneyball endures as one of the most successful examples of sports journalism to date. People magazine even called the book “the most influential book on sports ever written.”
Twenty years later, it’s time to reconsider Moneyball’s central inversion of the typical sports underdog story, focusing not on the players who step up to the plate but instead on their “scrappy” managers assessing their values on spreadsheets. There’s something menacing in it, something a little too eager to subordinate everything to the logic of finance, to take what’s human and hand it over to the algorithm. Perhaps we should rethink the beauty of that particular game.
Moneyball for Everything
Though Moneyball is remembered as a sports narrative, it is better characterized as a business handbook. When taken alongside the slew of books it inspired and influenced — for example, Jonah Keri’s The Extra 2%: How Wall Street Strategies Took a Major League Baseball Team from Worst to First, Ben Reiter’s bestseller Astroball: The New Way to Win It All, and Nate Silver’s famed book The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t — the language of business and financial markets is impossible to miss.
A player is a “valuable commodity”; moneyball strategies to “exploit market inefficiencies” include “positive arbitrage”; baseball plays are compared to financial “derivatives”; and the phrase “edge” is quite simply ubiquitous. In these books, the connection between moneyball approaches and broader business practices is even rendered explicit. In the forward to Keri’s The Extra 2%, Dallas Mavericks owner and Shark Tank “shark” Mark Cuban concludes with the proclamation, “No matter what kind of business you’re trying to run, you should read this book. Then you too can understand what the extra 2% is all about.”
It comes as no surprise, then, that the influence of Moneyball runs deep at business and management schools. Consider the Harvard Business Review’s essay “Moneyball and the Talent Mismatch Facing Business”; Wharton Magazine’s piece “‘Moneyball’ for Managers”; UC Davis School of Management’s blog on “Moneyball: Business Lessons on Value Creation”; and the MIT Sloan Executive Education course “Management Analytics: Decision-Making Lessons from the Sports Industry” (the school also hosts the Sloan Sports Analytics Conference).
A quick Google search returns articles and blog posts advocating for applications as far-ranging as “Moneyball for Arbitrators” to “Moneyball for Distributors” to “Moneyball for Motion Pictures” to “Medical Sales Moneyball.” The phrase “moneyball” is now a euphemism for data-driven management practices, where workers’ value is ruthlessly maximized under budgetary constraints — but hey, it’s all in good fun, because it’s moneyball!
Indeed, parallels between sports analytics and labor surveillance run deep. The Statcast camera systems used to track every pitch and swing in major league baseball stadiums (and even some minor league ones) are reminiscent of Amazon’s incredibly invasive monitoring of its drivers using cameras that issue real-time alerts, as covered by Alex Press in Jacobin. Likewise, the NFL Scouting Combine events where football prospects are brought out in skintight spandex and measured down to multiple significant digits have clear resonances with the chattel slavery auction block.
As argued by Robert Scoop Jackson in his book, The Game Is Not a Game: The Power, Protest, and Politics of American Sports, the rise of sports analytics is
the customary American process of controlling someone else’s American Dream. Prioritizing efficiency over creativity by monitoring the workforce to divert even more profits for owners and shareholders by “quantifying the labor” in a way that squeezes every last drop out of the people actually doing the work.
It is obvious, but the very linguistic turn from “baseball” to “moneyball” serves as a useful reminder about this emphasis on efficiency. Even in the context of professional sports, money is the bottom line, and every player is expendable.
Notably, Moneyball can be read as part of a broader trend surrounding sports spectatorship itself, marked by deepening ties with financial speculation. As argued by Andrew Schenker in the Baffler, it is “a short step from the abstractions of increasingly complex statistical models to the further abstractions of fantasy baseball and daily sports betting,” encouraging fans to treat players as commodities to be traded for direct monetary gain. As I’ve written for Current Affairs, even sports cards have skyrocketed in value, encouraging collectors to accurately assess prospects and future player value in order to speculate on card prices. And across sports video games including Madden and Football Manager, fans are invited to play the role of manager in franchise modes as its own form of entertainment. If business schools encourage us to adopt moneyball in professional management, sports spectatorship now encourages its own inescapable form of moneyball-infused armchair management.
One can trace a clear genealogy from baseball analytics to data-driven punditry, as evidenced by the forecaster Nate Silver himself, who first rose to prominence with his baseball analytics system PECOTA (“Player Empirical Comparison and Optimization Test Algorithm”). Silver would go on to create the highly successful blog-turned-publication FiveThirtyEight on the basis of successful election forecasting and data journalism inspired by sabermetric approaches.
Today, an unrelenting faith in analytics and its predictive power imbues our relationship with topics as far-reaching as politics and health care under this notion that everything can be quantified. The New York Times’s recent headline surrounding former Microsoft CEO Steve Ballmer’s efforts to build a “‘Moneyball’ for Government” suggests that moneyball-as-analogy is here to stay.
Tyranny of Spreadsheets
Twenty years after Moneyball, Michael Lewis’s broader journalistic project has become quite clear. Lewis’s 2010 bestseller The Big Short: Inside the Doomsday Machine picked up on Moneyball’s success, becoming the public’s focal point for understanding the 2008 financial crisis in layman’s terms (the book even followed Moneyball’s blueprint as a crossover Hollywood blockbuster).
The central problem with Moneyball, and The Big Short to boot, is not the writing itself — Lewis is clearly a talented storyteller with a knack for teasing out complex ideas with compelling narratives. But like The Big Short and Lewis’s 2014 book Flash Boys: A Wall Street Revolt, which position a handful of rogue traders and financial analysts as protagonists, Moneyball turns the managers into players to root for.
In this worldview, the new sports field is the financial market, and the new game is the thrilling quest to find “edge,” whether in the form of shorting failing subprime mortgage–backed securities or creating new efficiency metrics for evaluating labor. The numbers whizzes in Lewis’s narratives are the little guys, up against lumbering, cartoonishly evil corporate behemoths. But what about those other scrappy underdogs? Rather than encouraging us to consider the ways in which baseball players themselves are exploited as laborers, Lewis leaves us with an invitation to play manager and fantasize about fulfilling our own quest to “find edge” by peering past the human, deep into the stats and figures.
Michael Lewis has made a career out of those who see financial markets differently, and cash in on what they see. There’s no doubt that his subjects have special, enviable abilities. But do they really leave the world better? The mechanization, quantification, and financialization of everything undoubtedly renders life less warm and dimensional, cramming it into spreadsheets while also fine-tuning the machines of exploitation and inequality. Maybe such stories are actually cautionary tales, in which case we’re cheering at our own submission to the tyranny of numbers with dollar signs in front of them.