Sports result prediction using data mining techniques in comparison with base line model

Joseph, A., Fenton, N.E., Neil, M.: Predicting football results using Bayesian nets and other machine learning techniques. Knowl. Based Syst. 19(7), 544–553 (2006)

Delen, D., Cogdell, D., Kasap, N.: A comparative analysis of data mining methods in predicting NCAA bowl outcomes. Int. J. Forecast. 28(2), 543–552 (2012)

Kahn, J.: Neural network prediction of NFL football games. World Wide Web electronic publication 9–15 (2003)

Bunker, R.P., Thabtah, F.: A machine learning framework for sport result prediction. Appl. Comput. Inform. 15(1), 27–33 (2019)

Daud, A., et al.: Ranking cricket teams. Inf. Process. Manag. 51(2), 62–73 (2015)

Saqlain, S.M., Usmani, R.S.A.: Comment on “ranking cricket teams”. Inf. Process. Manag. 53(2), 450–453 (2017)

Agarwal, S., Yadav, L., Mehta, S.: Cricket team prediction with hadoop: statistical modeling approach. Procedia Comput. Sci. 122, 525–532 (2017)

Dey, P., Ganguly, M., Roy, S.: Network centrality based team formation: a case study on T-20 cricket. Appl. Comput. Inform. 13(2), 161–168 (2017)

Jhawar, M.G., Pudi, V.: Predicting the outcome of ODI cricket matches: a team composition based approach. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2016 2016) (2016)

Bailey, M., Clarke, S.R.: Predicting the match outcome in one day international cricket matches, while the game is in progress. J. Sports Sci. Med. 5(4), 480 (2006)

Bailey, M.J., Clarke, S.R.: Market inefficiencies in player head to head betting on the: cricket world cup. In: Economics, Management and Optimization in Sports. Springer, Berlin, Heidelberg, vol. 2004, pp. 185–201 (2003)

De Silva, B.M., Swartz, T.B.: Winning the coin toss and the home team advantage in one-day international cricket matches. Department of Statistics and Operations Research, Royal Melbourne Institute of Technology (1998)

Morley, B., Thomas, D.: An investigation of home advantage and other factors affecting outcomes in English one-day cricket matches. J. Sports Sci. 23(3), 261–268 (2005)

Bandulasiri, A.: Predicting the winner in one day international cricket. J. Math. Sci. Math. Educ. 3(1), 6 (2006)

Allsopp, P.E., Clarke, S.R.: Rating teams and analysing outcomes in one-day and test cricket. J. R. Stat. Soc. Ser. A (Stat. Soc.) 167(4), 657–667 (2004)

Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 2011, 2825–2830 (2011)

Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:1309.0238 (2013)

Rish, I.: An empirical study of the naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3. No. 22. IBM (2001)

Wymeersch, H., et al.: A machine learning approach to ranging error mitigation for UWB localization. IEEE Trans. Commun. 60(6), 1719–1728 (2012)

Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)

Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)

Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: International Conference on Extending Database Technology. Springer, Berlin, Heidelberg (1996)