The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Using random forests to estimate win probability before each play of an NFL game
Abstract
Before any play of a National Football League (NFL) game, the probability that a given team will win depends on many situational variables (such as time remaining, yards to go for a first down, field position and current score) as well as the relative quality of the two teams as quantified by the Las Vegas point spread. We use a random forest method to combine pre-play variables to estimate Win Probability (WP) before any play of an NFL game. When a subset of NFL play-by-play data for the 12 seasons from 2001 to 2012 is used as a training dataset, our method provides WP estimates that resemble true win probability and accurately predict game outcomes, especially in the later stages of games. In addition to being intrinsically interesting in real time to observers of an NFL football game, our WP estimates can provide useful evaluations of plays and, in some cases, coaching decisions.
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In the citing paper
“Lock and Nettleton (2014) implement a random forest framework to provide a win probability estimate before each play in a football game.”
From Big ideas in sports analytics and statistical tools for their investigation (2023) · cites this paper as Lock and Nettleton (2014)
In the citing paper
“According to this model, the difference in score between the two teams is the most important feature for predicting win probabilities at any given moment in an NFL game.”
From Big ideas in sports analytics and statistical tools for their investigation (2023) · cites this paper as Lock and Nettleton (2014)
Citations
Cites
Random forests for classification in ecology
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007) · Ecology · doi:10.1890/07-0539.1 · Cited by 4,950
Variable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619
Random Forests and Adaptive Nearest Neighbors
Lin, Y., & Jeon, Y. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001230 · Cited by 507
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- Freiman, 2010, Using random forests and simulated annealing to predict probabilities of election to the baseball hall of fame of Quantitative Analysis in, Journal Sports, 6, 1
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- Svetnik, 2003, Random forest : a classification and regression tool for compound classification and modeling of chemical information and computer, Journal sciences, 43, 1947
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- Genuer, 2010, Variable selection using random forests, Pattern Recognition Letters, 31, 14, 10.1016/j.patrec.2010.03.014 · DOI · Paper page
- Xu, 2014, Predictor augmentation in random forests Its InterfaceAccepted, Statistics
- Breiman, 2001, Statistical modeling the two cultures, Statistical Science, 16, 199, 10.1214/ss/1009213726 · DOI · Paper page
- Freiman, 2010, Using random forests and simulated annealing to predict probabilities of election to the baseball hall of fame of Quantitative Analysis in, Journal Sports, 6, 1
- Chandler, 2012, An exploratory study of minor league baseball of Quantitative Analysis in, statistics Journal Sports, 8, 1
- Buttrey, 2011, Estimating scoring rates of Quantitative Analysis in, Journal Sports, 7, 1
- Diaz, 2006, de selection and classification of microarray data using random forest, Gene Bioinformatics, 7, 1
- Fry, 2012, Searching for momentum in the of Quantitative Analysis in, Journal Sports, 8, 1
- Mills, 2011, Using tree ensembles to analyze National Baseball Hall of Fame voting patterns : an application to discrimination in BBWAA voting of Quantitative Analysis in, Journal Sports, 7, 1
- Mills, 2011, Using tree ensembles to analyze National Baseball Hall of Fame voting patterns : an application to discrimination in BBWAA voting of Quantitative Analysis in, Journal Sports, 7, 1
- Lin, 2006, Random forests and adaptive nearest neighbors of the, Journal American Statistical Association, 101
- Johnson, 2012, Turning the tide : big plays and psychological momentum in the MIT Conference, Sports
- Fry, 2012, Searching for momentum in the of Quantitative Analysis in, Journal Sports, 8, 1
Cited by
Sorted by citations
Big ideas in sports analytics and statistical tools for their investigation
Baumer, B. S., Matthews, G. J., & Nguyen, Q. (2023) · WIREs Computational Statistics · doi:10.1002/wics.1612 · Cited by 56
Metadata
- Authors
- Lock, D., & Nettleton, D.
- Venue
- Journal of Quantitative Analysis in Sports
- Publisher
- Walter de Gruyter GmbH
- Published
- 2014-01-01
- Volume
- 10
- Issue
- 2
- ISSN
- 1559-0410
- 2194-6388
- Cited by
- 34
- References
- 3
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