A hybrid ensemble learning framework for basketball outcomes prediction

Weihong Cai, Ding Yu, Ziyu Wu, Xin Du and Teng Zhou

Physica A: Statistical Mechanics and its Applications, 2019, vol. 528, issue C

Abstract: Basketball outcomes prediction is a vital technique for prospective player arrangement, injury avoidance, telecast right pricing, etc., which requires a understanding of the skill, luck, and other exterior factors of both teams. This paper presents a hybrid ensemble learning framework for basketball outcomes prediction by learning the recent status of the teams. To achieve this, we first design a new weighted combination feature for a future game by considering the latest status of the home team and the visiting team. Then, we present a hybrid ensemble framework equipped with bagging strategy and random subspace method to enlarge the diversity of the samples by learning a series of support vector machines. Finally, we develop a voting mechanism to predict the basketball outcomes. Extensive experiments have demonstrated the outperformance of our framework.

Keywords: Sport science; Basketball outcomes analysis; Team strategy; Ensemble learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:528:y:2019:i:c:s0378437119308507

DOI: 10.1016/j.physa.2019.121461

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