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Sports Data Mining: The Field

  • Robert P. Schumaker
  • Osama K. Solieman
  • Hsinchun Chen
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 26)

Abstract

Incredible amounts of data exist across all domains of sports. This data can come in the form of individual player performance, coaching or managerial decisions, game-based events and/or how well the team functions together. The task is not how to collect the data, but what data should be collected and how to make the best use of it. By finding the right ways to make sense of data and turning it into actionable knowledge, sports organizations have the potential to secure a competitive advantage versus their peers. This knowledge seeking approach can be applied throughout the entire organization. From players improving their game-time performance using video analysis techniques, to scouts using statistical analysis and projection techniques to identify what talent will provide the biggest impact, data mining is quickly becoming an integral part of the sports decision making landscape where manager/coaches using machine learning and simulation techniques can find optimal strategies for an entire upcoming season.

Keywords

Data Mining Technique Sport Organization National Football League National Pride Player Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer US 2010

Authors and Affiliations

  • Robert P. Schumaker
    • 1
  • Osama K. Solieman
    • 2
  • Hsinchun Chen
    • 3
  1. 1.Cleveland State UniversityClevelandUSA
  2. 2.TucsonUSA
  3. 3.University of ArizonaTucsonUSA

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