Enterprise Systems and Data Analytics: A Fantasy Football Case Study

Enterprise Systems and Data Analytics: A Fantasy Football Case Study

Carmine Sellitto (College of Business, Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Melbourne, Australia) and Paul Hawking (College of Business, Institute for Logistics and Supply Chain Management, Victoria University, Melbourne, Australia)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/IJEIS.2015070101
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Abstract

In sport there has been a reluctance to adopt new forms of technology unless it is associated with improving performance. However, in recent times, professional sport bodies have commenced using enterprise systems and data analytics for off-field activities. The authors use a case study approach in the paper to document the manner in which the National Football League (NFL) made available its vast array of historical and contemporary data to Fantasy Football participants through the adoption of an enterprise system that included in-memory processing and data analytics capabilities. Through a new player comparison tool, optimum player selections are made, enabling people to make more informed decisions on the management of their fantasy team. Furthermore, by making available its unique assortment of data to fantasy players, the NFL was able to expand its Fantasy Football platform. The paper provides insight into how other sporting bodies might be able to leverage their data capabilities.
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Literature Review

According to Alamar (2013), sport organizations are more likely to consider the use of data analytics tools in order to be competitive. Schumaker et al. (2010) highlight the vast amount of raw sport data that is typically collected on individual players, teams and game-related events. Furthermore, Schumaker et al. (2010) proposed a number of tiers in which sport-related data can be grouped based on the ways it can be used. At the simplest level, sports data can be collected around the operational aspects of a game as a means of keeping records of events or performance. Local and community-based sporting groups would typically fall under the realm of collecting sport data in this way (Bingley 2011). Other ways sport organizations might use data is to assist with sport decision making processes— where historical data or raw statistics are pre-cursors that allow people to be better informed about a particular sport situation. At the most advanced level, sport-related data can be analyzed so as to improve the activities that operate within a sport be they at an individual, team or organizational level which provides a new viewpoint or understanding of a situation (Schumaker et al. 2010).

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