CoChat
Sign Up
Cited by 34

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

Lock, Dennis, Nettleton, Dan·2014·Journal of Quantitative Analysis in Sports
DOI 10.1515/jqas-2013-0100
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1515/jqas-2013-0100

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 3 references

Research works printed but not linked: 31

  1. Chandler, 2012, An exploratory study of minor league baseball of Quantitative Analysis in, statistics Journal Sports, 8, 1
  2. Breiman, 2001, a Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
  3. Johnson, 2012, Turning the tide : big plays and psychological momentum in the MIT Conference, Sports
  4. Xu, 2014, Predictor augmentation in random forests Its InterfaceAccepted, Statistics
  5. Lindsey, 1961, The progress of the score during a baseball game of the, Journal American Statistical Association, 703, 10.1080/01621459.1961.10480656 · DOI
  6. Breiman, 2001, a Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
  7. Buttrey, 2011, Estimating scoring rates of Quantitative Analysis in, Journal Sports, 7, 1
  8. Hucaljuk, 2011, Predicting football scores using machine learning techniques Proceedings of the th International Convention, 1623
  9. Breiman, 2001, Statistical modeling the two cultures, Statistical Science, 16, 199, 10.1214/ss/1009213726 · DOI · Paper page
  10. Diaz, 2006, de selection and classification of microarray data using random forest, Gene Bioinformatics, 7, 1
  11. Cutler, 2007, Random forests for classification in, ecology Ecology, 11, 2783, 10.1890/07-0539.1 · DOI · Paper page
  12. 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
  13. Svetnik, 2003, Random forest : a classification and regression tool for compound classification and modeling of chemical information and computer, Journal sciences, 43, 1947
  14. Lindsey, 1961, The progress of the score during a baseball game of the, Journal American Statistical Association, 703, 10.1080/01621459.1961.10480656 · DOI
  15. Svetnik, 2003, Random forest : a classification and regression tool for compound classification and modeling of chemical information and computer, Journal sciences, 43, 1947
  16. Hucaljuk, 2011, Predicting football scores using machine learning techniques Proceedings of the th International Convention, 1623
  17. Stern, 1994, brownian motion model for the progress of sports scores of the, Journal American Statistical Association, 89
  18. Stern, 1994, brownian motion model for the progress of sports scores of the, Journal American Statistical Association, 89
  19. Genuer, 2010, Variable selection using random forests, Pattern Recognition Letters, 31, 14, 10.1016/j.patrec.2010.03.014 · DOI · Paper page
  20. Xu, 2014, Predictor augmentation in random forests Its InterfaceAccepted, Statistics
  21. Breiman, 2001, Statistical modeling the two cultures, Statistical Science, 16, 199, 10.1214/ss/1009213726 · DOI · Paper page
  22. 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
  23. Chandler, 2012, An exploratory study of minor league baseball of Quantitative Analysis in, statistics Journal Sports, 8, 1
  24. Buttrey, 2011, Estimating scoring rates of Quantitative Analysis in, Journal Sports, 7, 1
  25. Diaz, 2006, de selection and classification of microarray data using random forest, Gene Bioinformatics, 7, 1
  26. Fry, 2012, Searching for momentum in the of Quantitative Analysis in, Journal Sports, 8, 1
  27. 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
  28. 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
  29. Lin, 2006, Random forests and adaptive nearest neighbors of the, Journal American Statistical Association, 101
  30. Johnson, 2012, Turning the tide : big plays and psychological momentum in the MIT Conference, Sports
  31. Fry, 2012, Searching for momentum in the of Quantitative Analysis in, Journal Sports, 8, 1

Cited by

Sorted by citations

See all 34

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
DOI
10.1515/jqas-2013-0100
Cited by
34
References
3

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Using random forests to estimate win probability before each play of an NFL game. https://app.cochat.ai/papers/10.1515/jqas-2013-0100
The paper
Lock, D., & Nettleton, D. (2014). Using random forests to estimate win probability before each play of an NFL game. Journal of Quantitative Analysis in Sports. https://doi.org/10.1515/jqas-2013-0100