While it seems the term has been around baseball forever, the concept of “moneyball” — coined to describe the Oakland Athletics’ approach to building competitive teams despite being hamstrung with one of the sport’s lowest payrolls — entered the popular lexicon with Michael Lewis’ Moneyball: The Art of Winning an Unfair Game, in 2003 (followed by the film).
The concept is simple. Rather than relying on the traditional, often subjective wisdom of baseball-lifer scouts, who use their eyes to judge player ability, moneyball uses an analytical, evidence-based approach to build a roster.
It’s useful for cash-strapped teams because it typically identifies undervalued, overlooked players. The concept dates back to the days of sabermetrics — early analysis of nontraditional statistics that brought names like Bill James into the lexicon, and its use of “WAR” (wins above replacement) and emphasis on OBP (on-base percentage) rather than traditional measures of a player’s value, such as batting average and runs batted in.
For a while, it worked. The Athletics made the postseason each year from 2000 to 2003, going a stunning 103-59 in 2002. Once Lewis’ book hit, suddenly moneyball was everywhere – across all four major U.S. pro sports, and even in some teams’ business operations such as ticketing, other revenue streams and marketing.
Success breeds imitation, and some teams, such as the Boston Red Sox, combined moneyball concepts with their economic power to build rosters that merged overlooked assets with star players. Suddenly, the concepts that worked for small-market teams were co-opted by their major-market competitors.
If everyone is using the same analytics, does it give anyone an advantage?
Oakland never got past the League Championship Series, most recently playing in the ALCS in 2006. People soon grew tired of the analytical approach; it became passé, even mocked by some. A Deadspin piece bore the headline “Sports Analytics” Is Bullshit Now. It made a good point: If everyone is using the same analytics, does it give anyone an advantage?
These are increasingly crucial distinctions in sports, where the information available to all teams has basically become standardized. A few years ago, the best data was brokered by independent agencies selling it to teams individually. Now, the NBA subsidizes team access to SportVU player-tracking data, while MLB Advanced Media is rolling out its own fielding-tracking system, giving teams access to more data than most know what to do with.
That means there’s less and less low-hanging fruit for guys like Billy Beane, who were early to the notion of, well, simply bothering to look.
And in February, I was surprised to read a piece in the Boston Globe stating that owner John Henry, who made his living in commodities trading (“moneyball for soybeans”?), believes the team has relied too much on analytics in making major decisions. He also replaced his data-centric general manager with a baseball lifer, Dave Dombrowski, who judges talent in an old-school fashion.
This was a huge culture shift for the team. Despite a payroll either second or third highest in baseball, the Red Sox have also relied on statistics to build their roster. Former Sox player Kevin Youkilis, known as “The Greek God of Walks,” was featured extensively in Lewis’ book. And most of the time they’ve sought expensive free agents, the team has missed — mightily, from Daisuke Matsuzaka to Hanley Ramirez to Carl Crawford.
But with all that said, I had to ask myself, is the analytics era over?
As NFL referees would say, “upon further review” … no. In fact, I believe it’s still very much in its infancy, evolving as it finds peace with the established scouting methods and philosophies that have for so long dominated baseball.
From the Globe piece: “[S]couts (were) giving each other high-fives and wearing smiles ear to ear while the analytics community was taken aback. Henry’s declaration was shocking to them, while the scouting community saw the comments as a victory.”
That line is very telling, and displays the misconceptions “lifer” scouts have on data mining and analytics. The common belief is that scouts see the relationship between analytics and “feet-on-the-street scouting” as a zero-sum game: one approach has to win at the expense of another, a war of attrition between stat geeks like Bill James and old-school guys like Jim Leyland.
If I hit 100 fly balls, I know that 100 of them will fall toward the ground; thus, gravity works.
I believe this is because the analytics side of the house still hasn’t effectively communicated its importance to the larger scouting community. Currently, analytics is analogous to the physics professor you had in high school or college who filled a chalkboard explaining Newton’s law of gravity — and, as a student, you sat there wondering, why do I need to know all this? Things fall from the sky to the ground. I get it. Scouts are like those students who question why such learning is necessary. If I hit 100 fly balls, I know that 100 of them will fall toward the ground; thus, gravity works.
But once we stop seeing this as an all-or-nothing approach — and use the hybrid methodologies of successful teams like the Red Sox (2004, 2007 and 2013 World Series Champions) — success can be shared among an organization rather than credited to a siloed philosophy. The real challenge is learning how to encourage both sides to work together, as opposed to against each other, and fielding the best possible team with their budget.
This type of dichotomy and resistance to work together toward a common goal is not a foreign concept. In fact, it’s present just about everywhere you look: the corporate world, colleges and universities, politics and even in a preschool playpen. Don’t believe me? Put two toddlers in a room with only one truck and tell me how well they work together. Put two strong-willed groups of people in a room with only one way of operating, and why would you expect the results to be any different?
But baseball is not a zero-sum game, and it’s time to realize that both analytics approaches and traditional scouting offer value. They must work together so John Henry’s comments begin to sound not like a death knell for analytics, but the beginning of an era of cooperation.