Big Data Baseball: Math, Miracles, and the End of a 20-Year Losing Streak, Travis Sawchik, Flatiron Books, 256 pages, ISBN: 978-1250094254
Ever since Michael Lewis struck gold with his 2003 masterpiece Moneyball, baseball writers have sought to capture that book’s unique blend of underdog sports narrative and layered sociological commentary. One of the most recent entries in the “Moneyball Light” genre is the New York Times bestseller Big Data Baseball.
Embracing the Sabermetric Revolution
Written by former Pittsburgh Tribune-Review journalist Travis Sawchik, Big Data Baseball is a glimpse into the front office workings of baseball’s Pittsburgh Pirates. The Pirates endured one of the most miserable stretches in sports history, suffering twenty consecutive seasons with a losing record. But the club snapped that streak in 2013, and subsequently reached the playoffs in each of their next two seasons.
According to Sawchik, this turnaround was a result of a paradigm shift among Pittsburgh executives, who began assessing players with a variety of innovative new metrics. By hiring elite statisticians and embracing baseball’s big data movement, the small market Pirates found a way to compete with baseball’s more heavily funded juggernauts.
Big Data Baseball is a breezy read, with Sawchik’s matter of fact, unobtrusive prose relaying the story of how the Pirates went from perennial underachievers to postseason contenders. Sawchik describes the new analytics mavens hired by the Pirates, and how these statistical gurus have discovered under-the-radar talent by properly evaluating undervalued skills like pitch framing.
It’s evident that Sawchik has spent countless hours interviewing Pirates executives, and he explains the organization’s inner workings with the savvy of an insider. However, none of the practices described in Sawchik’s text comes across as revelatory.
Following the Moneyball Formula
The biggest issue with Big Data Baseball is that the idea of a small market ballclub triumphing over big market teams through superior intellect is no longer a unique premise. Moneyball captured the zeitgeist because it was published at a time when baseball’s old guard was resisting the statistical revolution that was about to reshape the game. Back in 2002, Billy Beane and the A’s were true iconoclasts for the way they rejected baseball norms.
In today’s game, however, every club is heavily dependent on analytics departments when it comes to roster construction and in-game strategies. As a result, this behind-the-scenes look at the Pirates fails to explain how Pittsburgh’s methodology differs from the other teams looking to gain a competitive advantage.
For the most part, Big Data Baseball is an enjoyable and informative read for baseball fans. Sawchik has a firm grasp on Pittsburgh’s use of analytics, and his explanations of stats are both concise and well-developed.
But while Moneyball was ahead of the curve when it came to the sabermetric movement, Big Data Baseball is not nearly as timely. The anecdotes surrounding key Pirates figures like manager Clint Hurdle lack flavor, and Sawchik has a tendency to repeat certain points (like the Pirates’ focus on infield shifts) to the point of irritation.
Big Data Baseball is an easily digestible text that is sure to delight Pirates fans. Unfortunately, those who lack familiarity with the franchise may find it difficult to become emotionally invested in the overarching narrative.
Want to take a crack at Big Data Baseball? Pick up your copy here!
