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Most Downloaded Articles
- Creating space to shoot: quantifying spatial relative field goal efficiency in basketball by Shortridge, Ashton/ Goldsberry, Kirk and Adams, Matthew
- A Starting Point for Analyzing Basketball Statistics by Kubatko, Justin/ Oliver, Dean/ Pelton, Kevin and Rosenbaum, Dan T
- Effect of position, usage rate, and per game minutes played on NBA player production curves by Page, Garritt L./ Barney, Bradley J. and McGuire, Aaron T.
NFL Prediction using Committees of Artificial Neural Networks
1The College of Wooster
2The College of Wooster
3The College of Wooster
4The College of Wooster
Citation Information: Journal of Quantitative Analysis in Sports. Volume 7, Issue 2, ISSN (Online) 1559-0410, DOI: 10.2202/1559-0410.1327, May 2011
- Published Online:
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.