Developing winning baseball teams: a neural net analysis Online publication date: Sun, 15-Feb-2009
by Owen P. Hall Jr., Samuel L. Seaman
International Journal of Sport Management and Marketing (IJSMM), Vol. 5, No. 3, 2009
Abstract: With an annual payroll of more than US$2 billion, major league baseball is big business. That being the case, it is not surprising that baseball's general managers are looking for ways to improve organisational performance. Neural networks have seen growing use in a variety of management applications, including finance, marketing, and operations. The purpose of this paper is to show how neural networks can be used to forecast individual and team outcomes as a means to help management improve overall organisational performance. The analytical paradigm described in this paper consists of a three-tiered hierarchical design that combines a number of specific on-field and off-field factors in predicting team performance. The results reveal that the developed model estimated the Runs Scored (RS) and the Runs Against (RA) within 5% of the actual performance over a five-year period. The paper also discusses some of the economic implications of implementing changes in the overall team structure.
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