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Predicting NBA Games Using Neural Networks


  • Loeffelholz Bernard

    (Air Force Institute of Technology)

  • Bednar Earl

    (Air Force Institute of Technology)

  • Bauer Kenneth W

    (Air Force Institute of Technology)


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

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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. 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.

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