Moneyball for Academics: Network Analysis for Predicting Research Impact

Bertsimas, D., Brynjolfsson, E., Reichman, S. and Silberholz, J., 2015. OR Forum—Tenure analytics: Models for predicting research impact. Operations Research, 63(6), pp.1246-1261.

17 Pages Posted: 5 Jan 2014 Last revised: 21 Nov 2018

See all articles by Dimitris Bertsimas

Dimitris Bertsimas

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Erik Brynjolfsson

National Bureau of Economic Research (NBER); Stanford

Shachar Reichman

Tel Aviv University - Coller School of Management

John Silberholz

University of Michigan, Stephen M. Ross School of Business

Date Written: January 4, 2014

Abstract

How are scholars ranked for promotion, tenure and honors? How can we improve the quantitative tools available for decision makers when making such decisions? Can we predict the academic impact of scholars and papers at early stages using quantitative tools?

Current academic decisions (hiring, tenure, prizes) are mostly very subjective. In the era of “Big Data,” a solid quantitative set of measurements should be used to support this decision process.

This paper presents a method for predicting the probability of a paper being in the most cited papers using only data available at the time of publication. We find that highly cited papers have different structural properties and that these centrality measures are associated with increased odds of being in the top percentile of citation count.

The paper also presents a method for predicting the future impact of researchers, using information available early in their careers. This model integrates information about changes in a young researcher’s role in the citation network and co-authorship network and demonstrates how this improves predictions of their future impact.

These results show that the use of quantitative methods can complement the qualitative decision-making process in academia and improve the prediction of academic impact.

Keywords: Citation analysis, Academic impact, Analytics, Networks

Suggested Citation

Bertsimas, Dimitris and Brynjolfsson, Erik and Reichman, Shachar and Silberholz, John, Moneyball for Academics: Network Analysis for Predicting Research Impact (January 4, 2014). Bertsimas, D., Brynjolfsson, E., Reichman, S. and Silberholz, J., 2015. OR Forum—Tenure analytics: Models for predicting research impact. Operations Research, 63(6), pp.1246-1261. , Available at SSRN: https://ssrn.com/abstract=2374581 or http://dx.doi.org/10.2139/ssrn.2374581

Dimitris Bertsimas

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

E53-359
Cambridge, MA 02142
United States
617-253-4223 (Phone)
617-258-7579 (Fax)

Erik Brynjolfsson

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Stanford ( email )

366 Galvez St
Stanford, CA 94305
United States

HOME PAGE: http://brynjolfsson.com

Shachar Reichman (Contact Author)

Tel Aviv University - Coller School of Management ( email )

Tel Aviv
Israel

HOME PAGE: http://https://en-coller.tau.ac.il/profile/sr

John Silberholz

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
1,603
Abstract Views
8,255
Rank
21,055
PlumX Metrics