References
- Clarke, S. R., & Dyte, D. (2000). Using official ratings to simulate major tennis tournaments. International transactions in operational research, 7(6), 585-594.10.1111/j.1475-3995.2000.tb00218.x
- Clowes, S., Cohen, G. L., & Tomljanovic, L. (2002). Dynamic evaluation of conditional probabilities of winning a tennis match. In Sixth Australian Conference on Mathematics and Computers in Sport. UTS.
- de Araujo Fernandes, M. (2017). Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Machine Learning Research, 2(3), 86-98.
- Klaassen, F. J., & Magnus, J. R. (2003). Forecasting the winner of a tennis match. European Journal of Operational Research, 148(2), 257-267.10.1016/S0377-2217(02)00682-3
- Knottenbelt, W. J., Spanias, D., & Madurska, A. M. (2012). A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers & Mathematics with Applications, 64(12), 3820-3827.10.1016/j.camwa.2012.03.005
- Ma, S. M., Liu, C. C., Tan, Y., & Ma, S. C. (2013). Winning matches in Grand Slam men’s singles: An analysis of player performance-related variables from 1991 to 2008. Journal of sports sciences, 31(11), 1147-1155.10.1080/02640414.2013.77547223458278
- McHale, I., & Morton, A. (2011). A Bradley-Terry type model for forecasting tennis match results. International Journal of Forecasting, 27(2), 619-630.10.1016/j.ijforecast.2010.04.004
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
- Sipko, M., & Knottenbelt, W. (2015). Machine learning for the prediction of professional tennis matches. MEng computing-final year project, Imperial College London.
- Wei, X., Lucey, P., Morgan, S., Reid, M., & Sridharan, S. (2016). The thin edge of the wedge:Accurately predicting shot outcomes in tennis using style and context priors. In Proceedings of the 10th Annu MIT Sloan Sport Anal Conf, Boston, MA, USA (pp.1-11).