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A hybrid ensemble learning framework for basketball outcomes prediction

Author

Listed:
  • Cai, Weihong
  • Yu, Ding
  • Wu, Ziyu
  • Du, Xin
  • Zhou, Teng

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.

Suggested Citation

  • Cai, Weihong & Yu, Ding & Wu, Ziyu & Du, Xin & Zhou, Teng, 2019. "A hybrid ensemble learning framework for basketball outcomes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    • Handle: RePEc:eee:phsmap:v:528:y:2019:i:c:s0378437119308507
    DOI: 10.1016/j.physa.2019.121461
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    Citations

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    Cited by:

    1. Sumit Sarkar & Sooraj Kamath, 2023. "Does luck play a role in the determination of the rank positions in football leagues? A study of Europe’s ‘big five’," Annals of Operations Research, Springer, vol. 325(1), pages 245-260, June.
    2. Fang, Weiwei & Zhuo, Wenhao & Yan, Jingwen & Song, Youyi & Jiang, Dazhi & Zhou, Teng, 2022. "Attention meets long short-term memory: A deep learning network for traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    3. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    4. Shumin Yang & Huaying Li & Zhizhe Lin & Youyi Song & Cheng Lin & Teng Zhou, 2022. "Quantitative Analysis of Anesthesia Recovery Time by Machine Learning Prediction Models," Mathematics, MDPI, vol. 10(15), pages 1-14, August.
    5. Peng, Yeping & Khaled, Usama & Al-Rashed, Abdullah A.A.A. & Meer, Rashid & Goodarzi, Marjan & Sarafraz, M.M., 2020. "Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validatio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

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