Moneyball for professors : models for predicting research impact

The use of quantitative methods in these processes is usually very limited. Given the stakes, and the boom of predictive analytics in the HR industry, we think it is time for a “Moneyball moment” in academia. Models predicting future outcomes can be used to support tenure decisions for early-career faculty. Moreover, our research finds that there’s strong potential for data-driven models to be used as decision aids for academic and financial committees that will improve selections. Researchers have previously examined scholarly work and tenure appointment, but the dial hasn’t moved much and traditional methods, which essentially count total numbers of citations, largely remain in place. Most notably, in 2005, J.E. Hirsch2 presented the case for the h-index. For this metric, a scientist is assigned an index of h, if h of her N papers contains at least h citations, and the other N h papers have no more than h citations each. Several later studies extended, modified, and offered alternatives to the h-index, such as including years in the field, graduate school attended, editorial board memberships, etc.3