Sports Data Mining: The Field
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.
KeywordsData Mining Technique Sport Organization National Football League National Pride Player Performance
- Almond, E. 1994. World Cup USA ‘94 Unforgiveable. The Los Angeles Times.Google Scholar
- Audi, T. & A. Thompson 2007. Oddsmakers in Vegas Play New Sports Role. The Wall Street Journal. A1.Google Scholar
- Ball, A. 2008. Winning by Numbers. The Guardian. London.Google Scholar
- Barry, D. 2009. Pappus’ Plane – Cricket Stats. Retrieved June 6, 2009, from http://pappubahry.blogspot.com.
- Brewer, P. 2009. I Don’t Like Cricket. Retrieved June 6, 2009, from http://sabermetriccricket.blogspot.com.
- CricInfo 2008. Wisden. Retrieved June 19, 2008, from http://content-www.cricinfo.com/wisdenalmanack/content/current/story/almanack/.
- CricketAnalysis.com 2009. Marginal Wins per Player in ODI Cricket. Retrieved June 6, 2009, from http:/cricketanalysis.com/marginal-wins-per-player-in-odi-cricket.
- Donegan, L. 2008. Tottenham Net Role in Revolution as Beane Pitches Success on a Budget. The Guardian. London.Google Scholar
- Dunshee, M. 2007. NHL Trade Deadline Revisited in Sabermetrics. Retrieved June 6, 2009, from http://www.associatedcontent.com/article/276051/nhl_trade_deadline_revisited_in_sabermetrics.html?cat=14.
- Fieltz, L. & D. Scott 2003. Prediction of Physical Performance Using Data Mining. Research Quarterly for Exercise and Sport 74(1): 1–25.Google Scholar
- Flinders, K. 2002. Football Injuries are Rocket Science. Vnunet.com. London.Google Scholar
- Goodman, A. 2005. The Market for Smart: Does Hockey Need Some PhD’s? Retrieved June 6, 2009, from http://www.traffick.com/2005/06/market-for-smart-does-hockey-need-some.asp.
- James, B. 1982. The Bill James Baseball Abstract. Ballantine Books, New York.Google Scholar
- Kuper, S. 2006. Soccer Against the Enemy. Nation Books, New York.Google Scholar
- Levin, R. & G. Mitchell, et al. 2000. The Report of the Independent Members of the Commissioner’s Blue Ribbon Panel on Baseball Economics. Major League Baseball.Google Scholar
- Lewis, M. 2003. Moneyball. W.W. Norton & Company, New York.Google Scholar
- Oliver, D. 2005. Basketball on Paper: Rules and Tools for Performance Analysis. Brassey’s Inc., Dulles, VA.Google Scholar
- Piatetsky-Shapiro, G. 2008. Difference between Data Mining and Statistics. Retrieved Oct 2, 2008, from http://www.kdnuggets.com/faq/difference-data-mining-statistics.html.
- Shilling, D. 2005. Hockey Project Rating. Retrieved June 6, 2009, from http://web.archive.org/web/20051222110731/http://members.shaw.ca/hbtn/player_study/hpr.htm.
- Xinhua News 2009. Ukraine’s Dynamo, Shakhtar among world’s top 10 soccer clubs. Xinhua. Beijing.Google Scholar
- Zimmerman, P. 1985. The New Thinking Man’s Guide to Pro Football. Simon and Schuster, New York.Google Scholar