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NBA Game Result Prediction Using Feature Analysis and Machine Learning

Author

Listed:
  • Fadi Thabtah

    (Manukau Institute of Technology)

  • Li Zhang

    (Manukau Institute of Technology)

  • Neda Abdelhamid

    (Auckland Institute of Studies)

Abstract

In the recent years, sports outcome prediction has gained popularity, as demonstrated by massive financial transactions in sports betting. One of the world’s popular sports that lures betting and attracts millions of fans worldwide is basketball, particularly the National Basketball Association (NBA) of the United States. This paper proposes a new intelligent machine learning framework for predicting the results of games played at the NBA by aiming to discover the influential features set that affects the outcomes of NBA games. We would like to identify whether machine learning methods are applicable to forecasting the outcome of an NBA game using historical data (previous games played), and what are the significant factors that affect the outcome of games. To achieve the objectives, several machine learning methods that utilise different learning schemes to derive the models, including Naïve Bayes, artificial neural network, and Decision Tree, are selected. By comparing the performance and the models derived against different features sets related to basketball games, we can discover the key features that contribute to better performance such as accuracy and efficiency of the prediction model. Based on the results analysis, the DRB (defensive rebounds) feature was chosen and was deemed as the most significant factor influencing the results of an NBA game. Furthermore, others crucial factors such as TPP (three-point percentage), FT (free throws made), and TRB (total rebounds) were also selected, which subsequently increased the model’s prediction accuracy rate by 2–4%.

Suggested Citation

  • Fadi Thabtah & Li Zhang & Neda Abdelhamid, 2019. "NBA Game Result Prediction Using Feature Analysis and Machine Learning," Annals of Data Science, Springer, vol. 6(1), pages 103-116, March.
    • Handle: RePEc:spr:aodasc:v:6:y:2019:i:1:d:10.1007_s40745-018-00189-x
    DOI: 10.1007/s40745-018-00189-x
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    References listed on IDEAS

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    1. Fadi Thabtah & Neda Abdelhamid, 2016. "Deriving Correlated Sets of Website Features for Phishing Detection: A Computational Intelligence Approach," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-17, December.
    2. Loeffelholz Bernard & Bednar Earl & Bauer Kenneth W, 2009. "Predicting NBA Games Using Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-17, January.
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    Citations

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    Cited by:

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    3. Rodolfo Metulini & Giorgio Gnecco, 2023. "Measuring players’ importance in basketball using the generalized Shapley value," Annals of Operations Research, Springer, vol. 325(1), pages 441-465, June.
    4. Poojan Thakkar & Manan Shah, 2021. "An Assessment of Football Through the Lens of Data Science," Annals of Data Science, Springer, vol. 8(4), pages 823-836, December.
    5. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    6. Manlio Migliorati & Marica Manisera & Paola Zuccolotto, 2023. "Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver’s four factors on the probability of winning a basketball game," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 271-293, March.
    7. Junjie Hou & Yuqi Zhang & Duo Su, 2024. "Unified Image Harmonization with Region Augmented Attention Normalization," Annals of Data Science, Springer, vol. 11(5), pages 1865-1886, October.
    8. Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
    9. Jun Woo Kim & Mar Magnusen & Seunghoon Jeong, 2023. "March Madness prediction: Different machine learning approaches with non‐box score statistics," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2223-2236, June.

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