Many statistics are kept in the game of baseball. The goal of this study is to discover a winning formula for a major league baseball team. That is, a key combination of team attributes that result in winning the pennant. Using team level data from 1871 through 2008, attribute relevance analysis focuses on the league winner attribute to select factors influencing the season’s outcome. The resulting dataset is data mined using classification to find the pathways to winning. Data clustering and association mining are used to discover hidden data patterns that answer the question: “What can we do to help our franchise win?” From the results of this analysis, factors effecting individual game and season outcomes are discovered. Applying these factors to an individual team’s training program allows the franchise to focus on specific winning concepts that lead teams to victory in the pennant race of the World Series.
|Presenter:||Gregory Swan (Undergraduate Student)|
|Topic:||Computer Information Systems|
|Time:||11:05 am (Session II)|