Authors: David D. Tung
In this paper, we use statistical data mining techniques to analyze a multivariate data set of career batting performances in Major League Baseball. Principal components analysis (PCA) is used to transform the high-dimensional data to its lower-dimensional principal components, which retain a high percentage of the sample variation, hence reducing the dimensionality of the data. From PCA, we determine a few important key factors of classical and sabermetric batting statistics, and the most important of these is a new measure, which we call Offensive Player Grade (OPG), that efficiently summarizes a player’s offensive performance on a numerical scale. The determination of these lower-dimensional principal components allows for accessible visualization of the data, and for segmentation of players into groups using clustering, which is done here using the K-means clustering algorithm. We provide illuminating visual displays from our statistical data mining procedures, and we also furnish a player listing of the top 100 OPG scores which should be of interest to those that follow baseball.
Comments: 30 Pages.
[v1] 2012-05-28 01:49:47
Unique-IP document downloads: 632 times
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.