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In-game win probability models for Canadian football

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  • S. E. Hill

Abstract

This article presents in-game win probability models for Canadian football. Play-by-play and wagering data for games from the Canadian Football League for the 2015 to 2019 seasons is used to create logistic regression and gradient boosting models. Models with and without the effect of pregame spread and total (over/under) data are presented and discussed. The resulting win probability models are well-calibrated and can be used to support in-game decision-making, review coaching decisions, estimate the magnitude of team “comebacks”, and potentially identify in-game wagering opportunities. An R Shiny application is provided to allow for estimation of in-game win probability for user-provided game state inputs. Opportunities for future work are identified and described.

Suggested Citation

  • S. E. Hill, 2022. "In-game win probability models for Canadian football," Journal of Business Analytics, Taylor & Francis Journals, vol. 5(2), pages 164-178, July.
    • Handle: RePEc:taf:tjbaxx:v:5:y:2022:i:2:p:164-178
    DOI: 10.1080/2573234X.2021.2015252
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    1. Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2021. "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Post-Print hal-03331805, HAL.
    2. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    3. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    4. Lijuan Mao & Zhaofang Peng & Hongyou Liu & Miguel-Angel Gómez, 2016. "Identifying keys to win in the Chinese professional soccer league," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 16(3), pages 935-947, December.
    5. Ciner, Cetin, 2021. "Stock return predictability in the time of COVID-19," Finance Research Letters, Elsevier, vol. 38(C).
    6. Lock Dennis & Nettleton Dan, 2014. "Using random forests to estimate win probability before each play of an NFL game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 197-205, June.
    7. Kent J. Kostuk & Keith A. Willoughby, 2012. "A Decision Support System for Scheduling the Canadian Football League," Interfaces, INFORMS, vol. 42(3), pages 286-295, June.
    8. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    9. Lopez Michael J. & Matthews Gregory J., 2015. "Building an NCAA men’s basketball predictive model and quantifying its success," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 5-12, March.
    10. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
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