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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Rigdon, Steve

Editorial Board Member: Glickman, PhD, Mark

4 Issues per year

CiteScore 2016: 0.44

SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358

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NFL Prediction using Committees of Artificial Neural Networks

John A. David / R. Drew Pasteur / M. Saif Ahmad / Michael C. Janning
Published Online: 2011-05-03 | DOI: https://doi.org/10.2202/1559-0410.1327

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.

Keywords: artificial neural networks; NFL; prediction

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Published Online: 2011-05-03

Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, DOI: https://doi.org/10.2202/1559-0410.1327.

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©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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