Over the past few years, the world of sports has experienced an explosion in the use of analytics. In this three-part series, we reflect on the current state of sports analytics and consider what the future of sports analytics may look like. We define sports analytics 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." Our definition is both expansive (in the sense that it includes not only statistical models but also the broader information value chain that surrounds these models) and restrictive (because it excludes traditional analytics applications such as demand forecasting, revenue management and financial modeling, all of which are certainly relevant in the business of professional sports). Data management includes any and all processes associated with acquiring, verifying and storing data in an efficient manner. In a sports organization, data can come from a variety of sources and may be presented in many different forms. The data management function will feed both the predictive analytics function and the information systems that support decision-makers. Given this crucial role, good data management is essential, and therefore missing, incomplete and/or inaccessible data inherently reduces the value of any other investments in analytics. In many organizations, data is often stored in isolated silos, so that getting data is often not a smooth process. Different groups within an organization such as scouting or training may have extensive data on players that other groups either do not have access to or do not even know exist. For example, the personnel group at one NFL team had been collecting extensive performance data on various groups of both opposing players and their own players. The coaching staff had no idea that the data existed, but when they did discover it, they had difficulty accessing it. The data resided in spreadsheets on the computers of the personnel group instead of being integrated into a common data archive. This is a common situation within professional sports organizations.
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