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NFL Prediction using Committees of Artificial Neural Networks

Author

Listed:
  • David John A.

    (The College of Wooster)

  • Pasteur R. Drew

    (The College of Wooster)

  • Ahmad M. Saif

    (The College of Wooster)

  • Janning Michael C.

    (The College of Wooster)

Abstract

This paper analyzes the ability of a neural network model to predict the outcome of NFL games. This model uses only readily available statistics, such as passing yards, rushing yards, fumbles lost, and scoring. A key component of this model is the use of statistical differentials to compare teams. For example, the offensive passing yards gained by one team are compared to the defensive passing yards allowed by an opposing team to create a data set of expected values for a given matchup. By using principal component analysis and derivative based analysis, we determined which statistics influence our model the most. We assessed the performance of the model by comparing its performance to that of published prediction algorithms and the Las Vegas oddsmakers over multiple seasons. Two novel aspects of this work include the use of multiple committees of machines for prediction and the use of our model to simulate virtual round-robin tournaments to establish an objective ranking of the teams.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:2:n:9
    DOI: 10.2202/1559-0410.1327
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    References listed on IDEAS

    as
    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
    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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    Cited by:

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    2. 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.

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