ABSTRACT
WHIP, FIP, BABIP, PECOTA, SCHOENE, PER, DVAR, QBR, GVT, Corsi: Each of these abbreviations and terms represents one component of what has come to be known as advanced metrics or analytics in sport. They all purport to allow sports fans and decision makers new quantitative ways of looking at otherwise familiar sports performances. Sports analytics has been defined as:
the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.
(Alamar and Mehrotra, 2011a, para. 2) In some respects, this definition remains somewhat unhelpful, in that this kind of intellectual activity has arguably been occurring in some way since the inception of modern sport (Guttmann, 1978) and, in some instances, in certain pre-modern sports as well (Carter and Kruger, 1990). However, the definition does speak to the more recent purposeful and widespread deployment of quantitative systems as a key cog in individual and organizational decision making in sports.