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Predicting NBA Games Using Neural Networks

Author

Listed:
  • Loeffelholz Bernard

    (Air Force Institute of Technology)

  • Bednar Earl

    (Air Force Institute of Technology)

  • Bauer Kenneth W

    (Air Force Institute of Technology)

Abstract

In this paper we examine the use of neural networks as a tool for predicting the success of basketball teams in the National Basketball Association (NBA). Statistics for 620 NBA games were collected and used to train a variety of neural networks such as feed-forward, radial basis, probabilistic and generalized regression neural networks. Fusion of the neural networks is also examined using Bayes belief networks and probabilistic neural network fusion. Further, we investigate which subset of features input to the neural nets are the most salient features for prediction. We explored subsets obtained from signal-to-noise ratios and expert opinions to identify a subset of features input to the neural nets. Results obtained from these networks were compared to predictions made by numerous experts in the field of basketball. The best networks were able to correctly predict the winning team 74.33 percent of the time (on average) as compared to the experts who were correct 68.67 percent of the time.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:5:y:2009:i:1:n:7
    DOI: 10.2202/1559-0410.1156
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    References listed on IDEAS

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    1. Page Garritt L & Fellingham Gilbert W & Reese C. Shane, 2007. "Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-18, October.
    2. Van Calster Ben & Smits Tim & Van Huffel Sabine, 2008. "The Curse of Scoreless Draws in Soccer: The Relationship with a Team's Offensive, Defensive, and Overall Performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(1), pages 1-24, January.
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    Cited by:

    1. Wei Gu & Thomas L. Saaty & Rozann Whitaker, 2016. "Expert System for Ice Hockey Game Prediction: Data Mining with Human Judgment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 763-789, July.
    2. 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.
    3. David John A. & Pasteur R. Drew & Ahmad M. Saif & Janning Michael C., 2011. "NFL Prediction using Committees of Artificial Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-15, May.
    4. Galli, L. & Galvan, G. & Levato, T. & Liti, C. & Piccialli, V. & Sciandrone, M., 2021. "Football: Discovering elapsing-time bias in the science of success," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    5. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    6. 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.
    7. 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.
    8. Hubáček, Ondřej & Šourek, Gustav & Železný, Filip, 2019. "Exploiting sports-betting market using machine learning," International Journal of Forecasting, Elsevier, vol. 35(2), pages 783-796.
    9. 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.
    10. Pierpalo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach," Annals of Operations Research, Springer, vol. 325(1), pages 419-440, June.

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