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A High-Performance Basketball Game Forecast Using Magic Feature Extraction

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Recent Advances in Data Science (IDMB 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1099))

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Abstract

It remains a challenge to predict real basketball game in a high-performance manner. In this study, we develop a magic feature extraction algorithm and its supporting techniques to tackle this problem. Our model achieves 79% stable accuracy for 2019’s NCAA and successfully predicts the black horses in the early rounds. To the best of our knowledge, this is the first systematic work to address NCAA basketball game prediction and will inspire the future work in this area.

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Author information

Authors and Affiliations

  1. The Gabelli School of Business, Fordham University, Lincoln Center, New York, NY, 10023, USA

    Tiange Li

  2. Computer and Information Science, Fordham University, Lincoln Center, New York, NY, 10023, USA

    Henry Han

  3. Department of Innovation and Entrepreneurship, Long Island University, Greenville, NY, 10458, USA

    Henry Han

  4. Academy of Plateau Science and Sustainability, School of Computer Science and Technology, Qinghai Normal University, Xining, 810008, China

    Henry Han

Authors
  1. Tiange Li
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  2. Henry Han
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Corresponding author

Correspondence to Henry Han .

Editor information

Editors and Affiliations

  1. Fordham University, New York, NY, USA

    Henry Han

  2. Guangxi University, Nanning, China

    Tie Wei

  3. Guangzhou University, Guangzhou, China

    Wenbin Liu

  4. Jiangsu University, Zhenjiang, China

    Fei Han

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Li, T., Han, H. (2020). A High-Performance Basketball Game Forecast Using Magic Feature Extraction. In: Han, H., Wei, T., Liu, W., Han, F. (eds) Recent Advances in Data Science. IDMB 2019. Communications in Computer and Information Science, vol 1099. Springer, Singapore. https://doi.org/10.1007/978-981-15-8760-3_3

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  • DOI: https://doi.org/10.1007/978-981-15-8760-3_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8759-7

  • Online ISBN: 978-981-15-8760-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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