By Caitlin Petre, Columbia Journalism Review DECEMBER 8, 2021
IN 2004, journalist Michael Lewis published Moneyball, a bestselling book about how baseball general manager Billy Beane was able to turn around the flailing and cash-poor Oakland Athletics with a clever use of statistics, culminating in a record-breaking 20-game winning streak. With its cast of beleaguered-underdogs-turned-triumphant-visionaries, Moneyball makes for a compelling story. It also turned out to be a tale perfectly suited to the dawning of the so-called “Big Data” era, in which new sources of analytics were supposed to provide strategic advantages for any organization savvy enough to leverage them.
It was, then, perhaps inevitable that the Moneyball mindset would be applied to the journalism field, especially as audience metrics became more granular and accessible.
But applying the Moneyball mindset to the actual work of journalism presents particular challenges. This is not only because some journalists remain wary of analytics as a potential force of displacement and usurpation. It is also due to the fact that the news industry differs in significant ways from professional baseball. A baseball team has a single, definable goal: to maximize wins. Furthermore, it is possible to know definitively if a team is making progress toward achieving that goal by looking at its record compared to those of other teams. Finally and perhaps most significant, all participants in the field agree unanimously about whether the goal has or has not been attained.
Journalism does not possess these traits. Commercial news organizations in democratic societies have multiple goals that are often in tension and difficult, if not impossible, to commensurate.