Michael Lewis’s 2003 book, Moneyball — later made into a movie starring Brad Pitt — tells the story of how predictive analytics transformed the Oakland Athletics baseball team and, eventually, baseball itself. Data-based modeling has since transcended sport. It’s used in hiring investment bankers, for example. But is academe really ready for its own “moneyball moment” in terms of personnel decisions?

A group of management professors from the Massachusetts Institute of Technology think so, and they’ve published a new study [Tenure Analytics: Models for Predicting Research Impact)] on a data-driven model they say is more predictive of faculty research success than traditional peer-based tenure reviews. In fact, several of the authors argue in a related essay [‘Moneyball’ for Professors?] in MIT Sloan Management Review that it’s “ironic” that “one of the places where predictive analytics hasn’t yet made substantial inroads is in the place of its birth: the halls of academia. Tenure decisions for the scholars of computer science, economics and statistics — the very pioneers of quantitative metrics and predictive analytics — are often insulated from these tools.”

Many professors oppose the use of bibliometrics in hiring, tenure and promotion decisions, saying that scholarly potential can’t be captured in a formula most often applied to pursuits with a bottom line, like winning games or playing the stock market. Such a system inevitably will be “gamed” by academics, critics say, and time-consuming but ground-breaking research will be sidelined in favor of “sure things” in terms of publishing — the academic equivalent of clickbait.

But in Sloan Review, the researchers argue that making data-based personnel decisions is in the public interest. “These decisions impact not just the scholars’ careers but the funding of universities and the overall strength of scientific research in private and public organizations as well,” they say. “On an individual level, a tenured faculty member at a prestigious university will receive millions of dollars in career compensation. At a broader scope, these faculty will bring funding into the universities that house them. The National Science Foundation, for instance, provided $5.8 billion in research funding in 2014, including $220 million specifically for young researchers at top universities.”

Bringing predictive analytics to any new industry means “identifying metrics that often have not received a lot of focus” and “how those metrics correlate with a measurable definition of success,” they note. In the case of academics, they say, important metrics are not just related to the impact of scholars’ past research but also details about their research partnerships and how their research complements the existing literature.

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