An in-game win probability uses the current “state” of a game/match to estimate the probability that a team will ultimately win the game/match. These types of models exist for a variety of sports.
I recently published an article on in-game win probability models for Canadian football in the “Journal of Business Analytics”. The article can be found online here: Article Link.
Below is a list of literature (organized by sport) related to in-game win probability models. This list is not intended to be inclusive of all relevant literature, but can be used as a starting point for exploring the literature around this topic.
Lindsey, G. R. (1961). The progress of the score during a baseball game. Journal of the American Statistical Association, 56(295), 703-728. https://www.tandfonline.com/doi/abs/10.1080/01621459.1961.10480656
Robberechts, P., Van Haaren, J., & Davis, J. (2019). Who will win it? An in-game win probability model for football. arXiv preprint arXiv:1906.05029. https://dtai.cs.kuleuven.be/events/MLSA19/papers/robberechts_MLSA19.pdf
Robberechts, P., Van Haaren, J., & Davis, J. (2021, August). A Bayesian Approach to In-Game Win Probability in Soccer. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3512-3521). https://dl.acm.org/doi/abs/10.1145/3447548.3467194?casa_token=jgJiwVw3IFgAAAAA:WKXBDtIhHQz7irqew9ZJtoNh8U97NE3J0dNtGyndIQpB7mGdls3Hc8mt206qQrIx6EATggY5S2KU
Klemp, M., Wunderlich, F., & Memmert, D. (2021). In-play forecasting in football using event and positional data. Scientific Reports, 11*(1), 1-10. https://www.nature.com/articles/s41598-021-03157-3
Stern, H. (1991). On the Probability of Winning a Football Game. The American Statistician, 45(3), 179-183. https://www.tandfonline.com/doi/10.1080/00031305.1991.10475798
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, 10(2), 197-205. https://www.degruyter.com/document/doi/10.1515/jqas-2013-0100/html?lang=de
Hill, S. E. (2021). In-game win probability models for Canadian football. Journal of Business Analytics, 1-15. https://www.tandfonline.com/doi/abs/10.1080/2573234X.2021.2015252
Highly Recommended: NFL win probability from scratch using xgboost in R (2021): https://www.opensourcefootball.com/posts/2021-04-13-creating-a-model-from-scratch-using-xgboost-in-r/
Maddox, J., Sides, R., & Harvill, J. (2022). Bayesian estimation of in-game home team win probability for college basketball. arXiv preprint arXiv:2204.11777. https://arxiv.org/abs/2204.11777
Guan, T., Nguyen, R., Cao, J., & Swartz, T. (2022). In-game win probabilities for the National Rugby League. The Annals of Applied Statistics, 16(1), 349-367. https://projecteuclid.org/journals/annals-of-applied-statistics/volume-16/issue-1/In-game-win-probabilities-for-the-National-Rugby-League/10.1214/21-AOAS1514.short
Albert, J. (2015). Player evaluation using win probabilities in sports competitions. Wiley Interdisciplinary Reviews: Computational Statistics, 7(5), 316-325. https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wics.1358?casa_token=4U_5Aots6uoAAAAA%3AlRBscIjK_7VkczeOCrvGzXQjtAHWVGVQUWDPYQF_51LYkb2s0_vVJPUsbuOCodg6y3HkeqYDZL6y_Uw
Gambletron 2000: https://www.gambletron2000.com/