‘Moneyball’ for Professors?

Using analytics to improve hiring decisions has transformed industries from baseball to investment banking. So why are tenure decisions for professors still made the old-fashioned way?

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An MIT SMR initiative exploring how technology is reshaping the practice of management.
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How can we use information about an employee’s current performance to predict future performance? That’s one of the key questions addressed by the ever-growing use of predictive analytics as a human resources (HR) tool. The application of predictive analytics to the management of baseball teams — brought to life in the book (2003) and then movie (2011) “Moneyball” — made vivid the ways that data-based modeling can be used for more accurate talent acquisition and deployment. The idea that metrics could guide strategy by supporting intuitive decision making has created a boom in the use of predictive analytics in the HR industry.

Ironically, 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.

That may soon change. A study we conducted with Dimitris Bertsimas and Shachar Reichman, published in Operations Research, finds that data-driven models can significantly improve decisions for academic and financial committees. In fact, the scholars recommended for tenure by our model had better future research records, on average, than those who were actually granted tenure by the tenure committees at top institutions.

Tenure decisions have impacts that ripple far outside of university campuses. The choices of which scholars are offered permanent posts are the key HR judgments made by academic institutions in the United States. 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. 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.

Despite these factors, academic decision-making processes rely mainly on subjective assessments of candidates. We believe, though, that if analytics is given the opportunity to complement the tenure decision-making process by offering improved predictions about candidates’ future performance and scholarly research, businesses and the public will be better served by the academic community.


An MIT SMR initiative exploring how technology is reshaping the practice of management.
More in this series

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Comments (2)
"For example, the proposed models do not account for scholars’ service to their universities or their personalities — criteria that cannot be easily quantified."

It is highly likely that the personalities of scholars are correlated to their co-authorship network, since if the said scholars had particularly unfavourable personalities then their chances of being invited to co-author a paper would be lower.
Great analysis.  However, it misses a key point.  Baseball and investment banks have simple outcome metrics: runs earned and portfolio returns.  Academic success is more nuanced.  I have read many journal articles comparing numbers of business faculty publications--some even distinguish "A Journals", though that is an ill-defined concept.  However, that may not be the one-and-only measure of academic performance.  No one reads most academic journal articles in business, and few of those articles influence real business thinking.  I suspect few students would describe "the job to be done" by institutes of higher learning as "publishing articles".   For most professors, academic research and articles are not necessarily linked to the best education for their students.