For baseball fans, more than for fans of any other sport, statistics have always played a prominent role in their devotion to the national pasttime. Indeed, sabermetrics—statistical analyses of baseball performance—has burgeoned into an industry of its own, as Major League Baseball teams have staffed up sabermetrics teams with analytical experts in order to gain a competitive edge.
In their recently published book, The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball, Andrew Zimbalist, Robert A. Woods Professor of Economics, and Benjamin Baumer, visiting professor of mathematics and statistics, look deeply into the efficacy of sabermetrics. In particular, they bring into question the contention made in the 2003 bestseller Moneyball that sabermetrics can level the competitive playing field for professional teams with tighter budgets against the league’s elite.
Baumer and Zimbalist will discuss The Sabermetric Revolution on The Bill Newman Show on WHMP Radio (AM1400/1240, FM96.9) on Wednesday, Feb. 5, at 9:15 a.m. and will read from their book at Booklink in Thornes Marketplace, downtown Northampton, at 7 p.m. on Thursday, Feb. 6. Today, Zimbalist is a guest on Clubhouse Confidential on MLB television, airing at 3:30, 5:30 and 7:30 p.m. Meanwhile, listen to a podcast interview of Zimbalist on Ron Kaplan’s Baseball Bookshelf.
Zimbalist recently responded to questions about the book for the Gate.
Gate: Your book’s approach is impartial, but can you offer your opinion on the dependability of sabermetrics in producing or resulting in competitive success for an MLB team?
Andrew Zimbalist: Sabermetrics is the use of statistical techniques to analyze player performance and game strategy. It was employed successfully in the late 1990s and early 2000s by the Oakland A’s in combination with an assessment of market inefficiencies (i.e., bargains—cases in which the majority of teams are undervaluing a skill.) The A’s general manager, Billy Beane, was able to identify certain player attributes, such as a player’s ability to walk, that were not properly valued in the players’ market. Beane’s intelligence and approach enabled the low-payroll A’s to maintain high win percentages for several years. The problem is that once other teams realized what was going on, they quickly adopted Beane’s approach. The market overcorrected and walks became overvalued. Beane moved on to find other player attributes that were undervalued.
If statistical analysis leads you there, then great; if psychological profiling does the trick, that’s great too. The challenge for sabermetrics is that the low-hanging fruit has already been picked. It doesn’t require a Ph.D. to understand that on-base percentage (batting average plus walk rate) is a more reliable metric of offensive performance than batting average (Allan Roth wrote memos about this to the Dodgers’ Branch Rickey in 1944.) Now the teams are trying to develop complicated metrics to appraise true fielding prowess, encompassing not just fielding percentage but a player’s jump on the ball and his range. Accomplishing this in a meaningful way has been elusive for the most part. Yet most teams continue trying to do this. Other teams are emphasizing video evidence of performance or focusing on a player’s emotional make up and character.
The upshot is that, while sabermetrics has contributed to team performance over the last 10 or 15 years, its value is less clear today. Further complicating this assessment is the fact that teams are merging statistical analytics with video analytics and even psychological analytics.
So, as big data has made its appearance in baseball (and football, basketball, hockey and soccer) front offices, teams have increasingly turned to MBAs and Ph.D.s to help them decipher which numbers to pay attention to, which to ignore, and what it all means. To know where it will all end, you have to read our book.
Gate: Baseball fans are known for their devotion to and belief in statistics. What is it about baseball that draws the statistically inspired?
AZ: The beginnings of professional baseball and the box score (invented by Henry Chadwick) are almost coterminous. What’s unique about baseball in the world of team sports is that individual performance largely stands independent of team talent. One can be a great hitter, base runner, pitcher or fielder on a poor team. It is hard, if not impossible, to be a great quarterback on a poor team. The more individualistic nature of production in baseball provides ample temptation to measure player performance. This temptation becomes more compelling because the progress of a baseball game is observed through a series of a limited number of discrete circumstances (e.g., runner on first with one out, runner on first and second with one out, etc.). In fact, there are 24 possible base runner and out combinations in each half inning. Consider football: the ball can be at any of 100 yard lines (or half or quarter yard lines), each at different points horizontally across the field, and each at one of four downs. Or basketball, where the ball can be positioned at an almost infinite number of coordinates on the court.
In short, baseball lends itself to quantification and measurement. It is not surprising that the systematic practice of statistical analytics emerged first in baseball.
Gate: Since the sabermetrically inspired John Henry assumed ownership of the Boston Red Sox, the team has had its best run of success in its history (in terms of postseason success). Also, your table in Chapter 7, of teams with highest on base statistics, is filled with successful teams. Does the evidence support the Moneyball contention, that emphasis on sabermetrics is a dependable model for success?
AZ: The Red Sox did benefit from certain, rather elementary, statistical insights in assembling their rosters over the last 10 years. One such insight, for instance, is that players reach their physical peaks around age 28 or 29, and that it makes little sense to offer free agents over age 30 long-term contracts. That said, the Red Sox’ success goes considerably beyond simple sabermetric insights and even further astray from what is portrayed in the book and movie Moneyball. Michael Lewis is a very good storyteller, sometimes with an overactive imagination.
Gate: What inspired the collaboration with Ben Baumer in addressing this topic?
AZ: Ben is an old friend, someone who was the New York Mets’ key sabermetrician for eight years, a leading light on the subject, and now a colleague at Smith. I couldn’t have made a better choice.
Gate: What effect would you want this book to have?
AZ: The same effect I want all my books to have: I want it to engage the reader’s intellect and to inspire people to think in a more critical, nuanced and ultimately realistic way about the subject. If this book leads the U.S. to end its embargo of Cuba, all the better.