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

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Abstract

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.

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Acknowledgments

The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan for financially supporting this research under Contract Nos. NSC 101-2410-H-260-005-MY2, MOST 103-2410-H-260-020, MOST 104-2410-H-260-018 and MOST 104-2410-H-262-007.

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Authors and Affiliations

  1. Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan, ROC

    Ping-Feng Pai & Lan-Hung ChangLiao

  2. Department of Information Management, Lunghwa University of Science and Technology, No. 300, Sec. 1, Wanshou Road., Guishan, Taoyuan 333, Taiwan, ROC

    Kuo-Ping Lin

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  1. Ping-Feng Pai
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Correspondence to Ping-Feng Pai.

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Pai, PF., ChangLiao, LH. & Lin, KP. Analyzing basketball games by a support vector machines with decision tree model. Neural Comput & Applic 28, 4159–4167 (2017). https://doi.org/10.1007/s00521-016-2321-9

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