Game Winning Metrics: Data Mining, Applications, and Performance Measurement

As another professional sports season for baseball, football, soccer, basketball, ice hockey, dog and horse racing, Indy car or NASCAR auto racing comes to an end, their respective team managers and owners need to focus on their strategic competitive advantages and challenges at the start of the their respective off-season and determine which players they need to keep, trade, retire or acquire. Game theory, was first introduced in the early 20's. After John Nash received a Nobel Prize for this enhancement of the game theory, it became of even greater interest. Combining game theory with data mining has led to recognizing patterns and relationships within large amounts of data that could be used to serve as predictors of future outcomes. Michael Lewis published "Moneyball" in 2003. In his book, he explained how data mining of prior baseball player metrics was used to improve the Oakland A's team performance. Sport fans will be shown simplified examples of methods of how some sport clubs now use player metrics to provide greater certainty for sport teams and their owners in making the above cited player decisions along with an enhanced spectator experience for their cheering fans.