Moneyball 2.0: The New, Team-Oriented Study of Baseball

Instead of focusing on single players' stats, managers are increasingly trying to analyze the dynamics of entire franchises, a shift that is already helping MLB teams win.
The Texas Rangers' Ron Washington and Elvis Andrus congratulate Prince Fielder on a solo home run against the Colorado Rockies on May 8, 2014. (Tony Gutierrez / AP)

Baseball is a team sport, but it sure gets lonely at times.

The pitcher stands at the center, for example, spitting and pacing before thousands. The infield stays lively, but there’s stoicism in the faces of outfielders whose involvement in the game is either nil or total. When a fly ball isn’t plummeting towards the outfielder’s glove, he simply waits. Basketball has the free throw, and football the field goal, but no other team sport is so composed of discrete events whose outcome is solely on the shoulders of individual players. In baseball, if the ball’s on its way, it’s up to you (and no one else) to do with it what you will.

Perhaps because baseball is made up of separable, individual-centric events, that’s the direction researchers went when, decades ago, they started thinking analytically about baseball. They sought to figure out how good a player was by isolating his performance, inching toward a single measurement quantifying a player’s contribution to his team.

Today, that metric exists. Wins Above Replacement, or WAR, is the culmination of the first wave of “sabermetrics,” the study of why teams win and lose baseball games—mature fruit of an idea first planted by Bill James in 1977 and watered, tended, and pruned by statisticians and analysts like Mike Gimbel (in 1990), Keith Woolner (1995), and Voros McCracken (2001). It’s a fairly intuitive concept: Take all the runs a player contributes at the plate, on the basepaths, and in the field, then calculate the additional games a team would win should they have this player rather than a scrub from AAA. It’s what the Society for American Baseball Research (or SABR) probably always dreamed about: a single metric that communicates, in one glance, a rough understanding of the value a player added to his team.

However, the smartest teams aren’t stopping there. Instead, they are changing their analysis to look at the team as a whole. How is player performance impacted by environment—by teammates, by ballpark, by managers? If the first wave of sabermetrics was largely about finding a holistic metric for comparing players, the next wave might be about figuring out how best to fit those players together as a response to a given situation. The key question from Moneyball 1.0 was: Who is the better player? The key questions from Moneyball 2.0 are: Who is the right player? And what is the best way to deploy him?

Some MLB teams are already making an effort to grab the two-percent advantage associated with the use of these advanced metrics—and those who are are already winning pennants.


The boldest transaction of the 2013-2014 offseason was a simple swap of two MLB All-Stars: Detroit’s Prince Fielder and Texas’s Ian Kinsler. Fielder is a burly first baseman with a home-run title. Kinsler is a second baseman who does a little bit of everything: He hits for a decent average with some power, steals a couple bags, and holds his own with the glove.

This year, Fielder will make 50 percent more than Kinsler ($24M to $16M), which we might have guessed by looking at their cumulative MVP shares (1.75 to 0.6, per Baseball-Reference). We wouldn’t have guessed the pay difference, though, if we were looking at their WAR totals).


Home runs make highlights; stolen bases, errorless streaks, and, most famously, walks accrete silently over the course of a season. WAR collects those quiet contributions to reveal how similarly valuable Kinsler and Fielder are, despite the pay gap. In that vein, it’s possible to look at the Fielder-Kinsler trade as the material evidence of decades of unrecognized, often amateur sabermetric work. Through a combination of number-crunching, abandonment of bias, and new technology, analysts have stripped away noise to focus on what is instructive and not just what is memorable.

That progress isn’t done, though. For example, fancy camerawork and smart thinking are rectifying one of the game’s quiet injustices: the accounting oversight that gives all the credit for a strikeout looking to the pitcher. Fans traditionally notch a backwards K for the pitcher while annotating no credit to the catcher, even though that catcher may have pulled a ball back to the plate and tricked the umpire in the process. Research on pitch-framing, a catcher’s ability to “receive and deceive,” suggests these stolen strikes add up to more than anyone realized—up to two wins per year for the best pitch-framers—marking an incremental advance on WAR to date. The best of these pitch-framers, Jose Molina, plays on the Tampa Bay Rays; since 2008, the Rays have won more than 90 games five times, despite a payroll consistently $100 million less than division rivals in Boston and New York.

Catchers won’t be the only ones whose defensive value will soon be better understood: MLB Advanced Media has announced plans to install world-class cameras in stadiums to track batted balls and defenders. The system will be able to calculate the efficiency of a defender’s route to the ball in near real-time, and can spit out an insane seven terabytes of data every game. It will help point the way toward the next analyses, not to mention vitiate Jim Caple’s most pointed criticism of WAR—that it is too reliant on fickle defensive metrics.

It’s true, though, that WAR isn’t everything, and can’t be everything. It would be wrong to let WAR’s new preeminence obscure radical new developments that take the in-game impact of sabermetrics to a new level. WAR can tell us who the best players are, but looking at that number alone won’t tell us much about the best way to deploy or arrange those players—what the optimal batting order might be, for example. Teams are now looking beyond individual players to how those players interact, making a leap from single- to multi-variable analysis and changing the game in the process.

To start, the new camera system will help teach defenses how to respond more dynamically to their environment. A football defense doesn’t employ the same configuration against Peyton Manning’s aerial assault and Adrian Peterson’s running game. Why should baseball operate any differently? The data from the camera system will accelerate the trend towards defensive shifting, a smarter deployment of personnel in which defenders are radically moved about the field depending on the batter’s tendencies. It’s not a new idea—Ted Williams, for one, was shifted on—but it is increasingly popular, and its frequency has increased threefold since 2010. The 2013 Pittsburgh Pirates shifted 425 times, fourth-most in the majors, en route to their first playoff berth since 1992.

It doesn’t take fancy camera technology to find another example of a responsive, team-level tactic. In fact, the second wave is perhaps best embodied by one of baseball’s oldest tricks: the platoon system, in which two players (one right-handed and the other left-handed) share a single defensive position. The platoon advantage is a rule of thumb by which we can guess that, when the batter and pitcher have opposite handedness, the batter has an advantage. It’s best to have batters who can hit right- and left-handed pitchers, but those hitters tend to be pricey; instead of splurging, spending-averse teams from Cleveland and Oakland get more production for less money by finding flawed specialists but putting them in the right environment to succeed.

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Hayden Higgins does strategy research at National Journal.

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