The last decade has seen a statistical revolution in sports, where new, smarter measures of player performance in baseball, football, or soccer are replacing more traditional stats. Often known as “sabermetrics” in tribute to the Society for American Baseball Research, advanced statistics such as VORP, BABIP, and FIPS try to more accurately quantify a player’s performance, while forecasting tools such as PECOTA try to predict their future. While imperfect, these stats have given general managers new tools to decide which players to sign to long-term contracts and which to release.
The scientific community has its own measures of career performance, but the use of these figures in personnel decisions remains controversial. Decisions on hiring or tenure remain largely in the hands of committees, who judge applicants based on their CV, interviews, pedigree, or myriad other potentially subjective factors. Attempts to come up with more objective measures of scientific achievement are handicapped by disagreement over what factors make a “good” scientist and predict a successful career: is it number of publications, or citations, or something else entirely?