Analyzing basketball games by a support vector machines with decision tree model.

Support vector machines (SVMs) are an emerging and powerful technique in coping with classification problems. However, a lack of rule generation is a weakness of the SVM model, especially in analyzing sporting results. This investigation developed a hybrid model integrating the SVM technique and a decision tree approach (HSVMDT) to predict the results of basketball games, and to provide rules to aid coaches in developing strategies. The HSVMDT model employed the unique strength of SVM and decision tree in generating rules and predicting the outcomes of games. With predicted outcomes of games, and rules yielded from the HSVMDT model, coaches can easily and quickly learn essential factors increasing the chances to win games. Empirical results showed that the proposed HSVMDT model can obtain relatively satisfactory prediction accuracy and therefore is a promising alternative for analyzing the results of basketball competitions. [ABSTRACT FROM AUTHOR]