In the 21st century, baseball has experienced two watersheds. First came the performance-enhancing-drug scandals. In 2002, Sports Illustrated published an explosive cover story in which the 1996 National League MVP, Ken Caminiti, confessed to rampant steroid use. In 2005, Congress got involved, and in 2019, two of the very best players in the history of the sport—Barry Bonds and Roger Clemens—remain unofficially barred from its Hall of Fame.
The second upheaval was fueled not by pharmaceutical science but by mathematics. The statistics-based movement commonly referred to as “sabermetrics” (or, more broadly, “analytics”) burst onto the scene just a year after Caminiti’s confession, with the publication of Michael Lewis’s best-selling Moneyball: The Art of Winning an Unfair Game, a wildly entertaining journey into the renegade tactics of the improbably successful Oakland A’s under the management of Billy Beane. Moneyball introduced readers to the theories of Bill James, the founder of sabermetrics (and coiner of the term), who catalyzed a reconception of the game as a contest determined by matching minutely calibrated player skills to particular game situations. Lewis’s book was immensely controversial upon its release, but Jamesian acolytes such as Rob Neyer, Keith Law, and Christina Kahrl rank among today’s most influential baseball writers. Acronyms like WHIP (walks and hits per inning pitched), FIP (fielding independent pitching), and WAR (wins above replacement) are now commonplace, if still fuzzily understood by casual fans.
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The steroid and stat revolutions have unfolded very differently so far, but they arose from a common source: a desire to deploy scientific methods to improve the way baseball is played. Steroid use focused on player enhancement, whereas analytics focused on player value. To put it polemically, one was a revolution driven by labor, and the other by management, which is probably one of many reasons the latter has been more readily accepted.
In The MVP Machine: How Baseball’s New Nonconformists Are Using Data to Build Better Players, Ben Lindbergh and Travis Sawchik set out to introduce the world to what they herald as yet another revolution, which represents a synthesis of sorts. Writers at The Ringer and FiveThirtyEight, respectively, they are at once steeped in advanced analytics and fixated on player improvement. As their subtitle indicates, they explore a growing movement within baseball to use statistical metrics, biomechanical data, and cutting-edge forms of player observation to help players hone their skills.
Their book is explicitly cast in the mold of Moneyball, to which the authors devote a substantial portion of their opening chapter. As Lindbergh and Sawchik rightly point out, Lewis had surprisingly little to say about player development. The philosophy that Beane brought to the A’s organization was guided not so much by what players could be as by what they were—it was about how to construct a roster out of players whose specific usefulness had been undervalued in the market. The Beane model didn’t have much to offer players who were interested in actually improving, aside from maybe “try walking more” and “don’t bunt so much.” By contrast, “this new phase is dedicated to making players better,” Lindbergh and Sawchik write. “It’s Betterball. And it’s taking over.”
