A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL

Beal, R., Norman, T., & Ramchurn, S. (2019). Artificial intelligence for team sports: a survey. The Knowledge Engineering Review, 34.10.1017/S0269888919000225Search in Google Scholar

Boulier, B., & Stekler, H. (2003). Predicting the outcomes of National Football League games. International Journal of Forecasting, 257-270.10.1016/S0169-2070(01)00144-3Search in Google Scholar

Clarke, S., & Norman, J. (1995). Home ground advantage of individual clubs in English soccer. Journal of the Royal Statistical Society: Series D (The Statistician), 509-521.Search in Google Scholar

Constantinou, A., Fenton, N., & Neil, M. (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Knowledge-Based Systems, 322-339.10.1016/j.knosys.2012.07.008Search in Google Scholar

Crowder, M., Dixon, M., Ledford, A., & Robinson, M. (2002). Dynamic modelling and prediction of English Football League matches for betting. Journal of the Royal Statistical Society: Series D (The Statistician), 157-168.10.1111/1467-9884.00308Search in Google Scholar

Dixon, M., & Coles, S. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 265-280.Search in Google Scholar

Dixon, M., & Robinson, M. (1998). A birth process model for association football matches. Journal of the Royal Statistical Society: Series D (The Statistician), 523-538.10.1111/1467-9884.00152Search in Google Scholar

Glickman, M., & Stern, H. (1998). A state-space model for National Football League scores. Journal of the American Statistical Association, 25-35.10.1080/01621459.1998.10474084Search in Google Scholar

Han, S., Qubo, C., & Meng, H. (2012). Parameter selection in SVM with RBF kernel function. World Automation Congress 2012, 1-4.Search in Google Scholar

Harville, D. (1977). The use of linear-model methodology to rate high school or college football teams. Journal of the American Statistical Association, 72, 278-289.10.1080/01621459.1977.10480991Search in Google Scholar

Harville, D. (1980). Predictions for National Football League games via linear-model methodology. Journal of the American Statistical Association, 516-524.10.1080/01621459.1980.10477504Search in Google Scholar

Joseph, A., Fenton, N., & Neil, M. (2006). Predicting football results using Bayesian nets and other machine learning techniques. Knowledge-Based Systems, 544-553.10.1016/j.knosys.2006.04.011Search in Google Scholar

Landers, J., & Duperrouzel, B. (2018). Machine learning approaches to competing in fantasy leagues for the NFL. IEEE Transactions on Games, 159-172.Search in Google Scholar

Maher, M. (1982). Modelling association football scores. Statistica Neerlandica, 109-118.10.1111/j.1467-9574.1982.tb00782.xSearch in Google Scholar

McCabe, A., & Trevathan, J. (2008). Artificial intelligence in sports prediction. Fifth International Conference on Information Technology: New Generations (S. 1194-1197). IEEE.Search in Google Scholar

Sankaranarayanan, V. V., Sattar, J., & Lakshmanan, L. (2014). Auto-play: A data mining approach to ODI cricket simulation and prediction. Proceedings of the 2014 SIAM International Conference on Data Mining, (S. 1064-1072).10.1137/1.9781611973440.121Search in Google Scholar

Suykens, J., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 293-300.10.1023/A:1018628609742Search in Google Scholar

Yang, T., & Swartz, T. (2004). A two-stage Bayesian model for predicting winners in major league baseball. Journal of Data Science, 61-73.Search in Google Scholar