[Angelini, G., & De Angelis, L. (2018). Efficiency of online football betting markets. International Journal of Forecasting, 35(2), 712-721. doi: 10.1016/j.ijforecast.2018.07.00810.1016/j.ijforecast.2018.07.008]Open DOISearch in Google Scholar
[Bhargava, N., Sharma, G., Bhargava, R., & Mathuria, M. (2013). Decision tree analysis on j48 algorithm for data mining. Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, 3(6).]Search in Google Scholar
[Bishop, C. (2006). Pattern recognition and machine learning. New York: Springer.]Search in Google Scholar
[Box, G., & Cox, D. (1964). An Analysis of Transformations. Journal Of The Royal Statistical Society Series B, 26(2), 211-252.10.1111/j.2517-6161.1964.tb00553.x]Search in Google Scholar
[Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.10.1023/A:1010933404324]Search in Google Scholar
[Buursma, D. (2011). Predicting Sports Events From Past Results Towards Effective Betting On Football Matches. In 14Th Twente Student Conference On IT, Twente, Holland (Vol. 21).]Search in Google Scholar
[Constantinou, A. C. (2018). Dolores: A model that predicts football match outcomes from all over the world. Machine Learning, 108(1), 49-75.10.1007/s10994-018-5703-7]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, 36, 322-339. doi: 10.1016/j.knosys.2012.07.008.10.1016/j.knosys.2012.07.008]Open DOISearch 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), 51(2), 157-168. doi: 10.1111/1467-9884.00308.10.1111/1467-9884.00308]Open DOISearch in Google Scholar
[Dixon, M., & Pope, P. (2004). The value of statistical forecasts in the UK association football betting market. International Journal Of Forecasting, 20(4), 697-711. doi: 10.1016/j.ijforecast.2003.12.007.10.1016/j.ijforecast.2003.12.007]Open DOISearch in Google Scholar
[Dobravec, S. (2015, May). Predicting sports results using latent features: A case study. In 2015 38th International Convention On Information And Communication Technology, Electronics And Microelectronics (MIPRO) (pp.1267-1272). IEEE. doi: 10.1109/mipro.2015.7160470.10.1109/mipro.2015.7160470]Open DOISearch in Google Scholar
[Domingos, P. (1999). MetaCost: a general method for making classifiers cost-sensitive. Proceedings of the 5th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining (KDD'99), (pp.155-164). doi:10.1145/312129.312220.10.1145/312129.312220]Open DOISearch in Google Scholar
[Elkan, C. (2001). The Foundations Of Cost-Sensitive Learning. Proccedings of the 17th international joint conference on Artificial Intelligence (pp. 973-978). Seattle, WA, USA.]Search in Google Scholar
[Eryarsoy, E., & Delen, D. (2019, January). Predicting the Outcome of a Football Game: A Comparative Analysis of Single and Ensemble Analytics Methods. Proceedings of the 52nd Hawaii International Conference on System Sciences. doi: 10.24251/HICSS.2019.13610.24251/HICSS.2019.136]Search in Google Scholar
[Football Results. (2018). Football-data.co.uk. Retrieved 7 September 2018, from http://www.football-data.co.uk/.]Search in Google Scholar
[Forrest, D., Goddard, J., & Simmons, R. (2005). Odds-setters as forecasters: The case of English football. International Journal Of Forecasting, 21(3), 551-564. doi: 10.1016/j.ijforecast.2005.03.003.10.1016/j.ijforecast.2005.03.003]Open DOISearch in Google Scholar
[Franck, E., Verbeek, E., & Nüesch, S. (2010). Prediction accuracy of different market structures—bookmakers versus a betting exchange. International Journal of Forecasting, 26(3), 448-459.10.1016/j.ijforecast.2010.01.004]Search in Google Scholar
[Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I., & Trigg, L. (2017). Weka. Data Mining And Knowledge Discovery Handbook, (pp. 1305-1314). doi:10.1007/0-387-25465-x_62.10.1007/0-387-25465-x_62]Open DOISearch in Google Scholar
[Goddard, J. (2005). Regression models for forecasting goals and match results in association football. International Journal Of Forecasting, 21(2), 331-340. doi: 10.1016/j.ijforecast.2004.08.002.10.1016/j.ijforecast.2004.08.002]Open DOISearch in Google Scholar
[Goddard, J., & Asimakopoulos, I. (2004). Forecasting football results and the efficiency of fixed-odds betting. Journal Of Forecasting, 23(1), 51-66. doi: 10.1002/for.877.10.1002/.877]Open DOISearch in Google Scholar
[Godin, F., Zuallaert, J., Vandersmissen, B., De Neve, W., & Van de Walle, R. (2014). Beating the bookmakers: leveraging statistics and Twitter microposts for predicting soccer results. Workshop on Large-Scale Sports Analytics, Proceedings. Presented at the Workshop on Large-Scale Sports Analytics (KDD 2014).]Search in Google Scholar
[Haaren, J., & Broeck, G. (2014). Relational Learning for Football-Related Predictions. Latest Advances in Inductive Logic Programming, 237-244.10.1142/9781783265091_0025]Search in Google Scholar
[Haaren, J., & Davis, J. (2015). Predicting The Final League Tables Of Domestic Football Leagues. Proceedings of the 5th International Conference On Mathematics In Sport, (pp. 202-207).]Search in Google Scholar
[Haghighat, M., Rastegari, H., & Nourafza, N. (2013). A Review Of Data Mining Techniques For Result Prediction In Sports. Advances In Computer Science: An International Journal, 2(5), 7-12.]Search in Google Scholar
[Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10-18. doi: 10.1145/1656274.1656278.10.1145/1656274.1656278]Open DOISearch in Google Scholar
[Hand, D., & Yu, K. (2001). Idiot's Bayes: Not So Stupid after All?. International Statistical Review / Revue Internationale De Statistique, 69(3), 385. doi: 10.2307/1403452.10.2307/1403452]Open DOISearch in Google Scholar
[Karlis, D., & Ntzoufras, I. (2003). Analysis of sports data by using bivariate Poisson models. Journal Of The Royal Statistical Society: Series D (The Statistician), 52(3), 381-393. doi: 10.1111/1467-9884.00366.10.1111/1467-9884.00366]Open DOISearch in Google Scholar
[Karlis, D., & Ntzoufras, I. (2008). Bayesian modelling of football outcomes: using the Skellam's distribution for the goal difference. IMA Journal Of Management Mathematics, 20(2), 133-145. doi: 10.1093/imaman/dpn026.10.1093/imaman/dpn026]Open DOISearch in Google Scholar
[Koopman, S., & Lit, R. (2013). A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League. Journal Of The Royal Statistical Society: Series A (Statistics In Society), 178(1), 167-186. doi: 10.1111/rssa.12042.10.1111/rssa.12042]Open DOISearch in Google Scholar
[Kyriakides, G., Talattinis, K., & George, S. (2014). Rating Systems Vs Machine Learning on the context of sports. Proceedings Of The 18th Panhellenic Conference On Informatics - PCI'14. doi: 10.1145/2645791.2645846.10.1145/2645791.2645846]Open DOISearch in Google Scholar
[Kyriakides, G., Talattinis, K., & Stephanides, G. (2017). A Hybrid Approach to Predicting Sports Results and an AccuRATE Rating System. International Journal Of Applied And Computational Mathematics, 3(1), 239-254. doi: 10.1007/s40819-015-0103-1.10.1007/s40819-015-0103-1]Open DOISearch in Google Scholar
[Kyriakides, G., Talattinis, K., & Stephanides, G. (2015). Raw Rating Systems and Strategy Approaches to Sports Betting. In 5th International Conference on Mathematics in Sport (pp. 97-102). Loughborough.]Search in Google Scholar
[McCarthy, K., Zabar, B., & Weiss, G. (2005). Does cost-sensitive learning beat sampling for classifying rare classes?. Proceedings Of The 1st International Workshop On Utility-Based Data Mining - UBDM'05 (pp.69-77). doi:10.1145/1089827.1089836.10.1145/1089827.1089836]Open DOISearch in Google Scholar
[Le, J. (2019). A Tour of The Top 10 Algorithms for Machine Learning Newbies. Retrieved 20 June from https://builtin.com/data-science/tour-top-10-algorithms-machine-learning-newbies.]Search in Google Scholar
[Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds). (2009). Machine Learning: Neural and Statistical classification. London: Overseas Press.]Search in Google Scholar
[Odachowski, K., & Grekow, J. (2013). Using Bookmaker Odds to Predict the Final Result of Football Matches. Lecture Notes In Computer Science, (pp. 196-205). doi:10.1007/978-3-642-37343-5_20.10.1007/978-3-642-37343-5_20]Open DOISearch in Google Scholar
[Provost, F., & Kohavi, R. (1998). Glossary of terms. Machine Learning, 30(2-3), (pp. 271-274).10.1023/A:1007442505281]Search in Google Scholar
[Schumaker, R. P., Jarmoszko, A. T., & Labedz Jr, C. S. (2016). Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decision Support Systems, 88(C), 76-84. doi: 10.1016/j.dss.2016.05.01010.1016/j.dss.2016.05.010]Open DOISearch in Google Scholar
[Scibilia, B. (2012). How Could You Benefit from a Box-Cox Transformation?. [The Minitab Blog.] Retrieved September 10 2018 from http://blog.minitab.com/blog/applying-statistics-in-quality-projects/how-could-you-benefit-from-a-box-cox-transformation.]Search in Google Scholar
[Sharpe, W. (1994). The Sharpe Ratio. The Journal Of Portfolio Management, 21(1), 49-58. doi: 10.3905/jpm.1994.40950110.3905/jpm.1994.409501]Open DOISearch in Google Scholar
[Sheng, V., & Ling, C. (2009). Cost-sensitive learning. In J. Wang, Encyclopedia of Data Warehousing and Mining (2nd ed.), (pp. 339-345).10.4018/978-1-60566-010-3.ch054]Search in Google Scholar
[Spann, M., & Skiera, B. (2009). Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters. Journal Of Forecasting, 28(1), 55-72. doi:10.1002/for.1091.10.1002/.1091]Open DOISearch in Google Scholar
[Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.10.1023/A:1018628609742]Search in Google Scholar
[Ting, K. (1998). Inducing cost-sensitive trees via instance weighting. Principles Of Data Mining And Knowledge Discovery, (pp. 139-147). doi:0.1007/bfb0094814.10.1007/BFb0094814]Search in Google Scholar
[Witten, I., & Frank, E. (2005). Data mining: practical machine learning tools and techniques (2nd ed.). San Francisco: Morgan Kaufmann.]Search in Google Scholar