Keywords and phrases: algorithm, classification, cross-validation, English Premier League, feature selection, machine learning, statistics.
Received: September 3, 2022; Revised: October 19, 2022; Accepted: December 5, 2022; Published: April 15, 2023
How to cite this article: Tomilayo P. Iyiola, Hilary I. Okagbue, Adedayo F. Adedotun and Toluwalase J. Akingbade, The effects of decomposition of the goals scored in classifying the outcomes of five English Premier League seasons using machine learning models, Advances and Applications in Statistics 87(1) (2023), 13-27. http://dx.doi.org/10.17654/0972361723026
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] V. S. Arrul, P. Subramanian and R. Mafas, Predicting the football players’ market value using neural network model: a data-driven approach, ICDCECE. 2022. https://doi.org/10.1109/ICDCECE53908.2022.9792681. [2] A. Majumdar, R. Bakirov, D. Hodges, S. Scott and T. Rees, Machine learning for understanding and predicting injuries in football, Sports Med. Open 8(1) (2022), Art. 79. [3] S. Jain, E. Tiwari and P. Sardar, Soccer result prediction using deep learning and neural networks, Lect. Notes Data Engine. Commun. Technol. 57 (2021), 697-707. [4] R. Beal, T. J. Norman and S. D. Ramchurn, Artificial intelligence for team sports: A survey. Knowl. Engine. Review (2019), e28. https://doi.org/10.1017/S0269888919000225. [5] U. Haruna, J. Z. Maitama, M. Mohammed and R. G. Raj, Predicting the outcomes of football matches using machine learning approach, Commun. Comp. Info. Sci. 1547 (2022), 92-104. [6] S. K. Andrews, K. L. Narayanan, K. Balasubadra and M. S. Josephine, Analysis on sports data match result prediction using machine learning libraries, J. Physics: Conf. Series, 1964(4) (2021), Art. 042085. [7] E. Wheatcroft, Evaluating probabilistic forecasts of football matches: the case against the ranked probability score, J. Quant. Analy. Sports 17(4) (2021), 273-287. [8] L. S. Benz and M. J. Lopez, Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression, Adv. Stat. Analy. (2021), https://doi.org/10.1007/s10182-021-00413-9. [9] T. Liu, A. García-de-Alcaraz, H. Wang, P. Hu and Q. Chen, Impact of scoring first on match outcome in the Chinese Football Super League, Front. Psych. 12 (2021), Art. 662708. [10] N. Razali, A. Mustapha, N. Mustapha and F. M. Clemente, A Bayesian approach for major European football league match prediction, Int. J. Nonlinear Anal. Appl. 12 (2021), 971-980. [11] A. C. Constantinou, N. E. Fenton and M. Neil, Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks, Knowl. Based Syst. 50 (2013), 60-86. [12] L. Carloni, A. De Angelis, G. Sansonetti and A. Micarelli, A machine learning approach to football match result prediction, Commun. Comp. Info. Sci. 1420 (2021), 473-480. [13] I. B. da Costa, L. B. Marinho and C. E. S. Pires, Forecasting football results and exploiting betting markets: the case of “both teams to score”, Int. J. Forecasting 38(3) (2022), 895-909. [14] A. Cortez, A. Trigo and N. Loureiro, Football match line-up prediction based on physiological variables: a machine learning approach, Computers 11(3) (2022), Art. 40. [15] A. Ranjan, V. Kumar, D. Malhotra, R. Jain and P. Nagrath, Predicting the result of English premier league matches, Lect. Notes Netw. Syst. 203 (2021), 435-446. [16] R. Nestoruk and G. Słowiński, Prediction of football games results, CEUR Workshop Proc., 2951, 2021, pp. 156-165. [17] P. Xenopoulos and C. Silva, Graph neural networks to predict sports outcomes, Proc. IEEE Int. Conf. on Big Data, 2021, pp. 1757-1763. [18] C. Pipatchatchawal and S. Phimoltares, Predicting football match result using fusion-based classification models, 18th Int. Joint Conf. on Comp. Sci. and Software Engine. Cybernet. Human Beings 2021, Art. 9493837. [19] A. M. Sánchez Gálvez, R. Álvarez González, S. Sánchez Gálvez and M. Anzures García, Model to predict the result of a soccer match based on the number of goals scored by a single team, Computacion y Sistemas 26(1) (2022), 295-302. [20] J. Fahey-Gilmour, J. Heasman, B. Rogalski, B. Dawson and P. Peeling, Can elite Australian football player’s game performance be predicted? Int. J. Comp. Sci. Sport 20(1) (2021), 55-78. [21] Y. Bai and X. Zhang, Prediction model of football world cup championship based on machine learning and mobile algorithm, Mobile Information Systems 2021 (2021), Art. 1875060. [22] J. Yadav, Fuzzy C-mean clustering based soccer result analysis, Comm. Comp. Info. Sci. 1572 (2022), 3-14. [23] I. Behravan and S. M. Razavi, A novel machine learning method for estimating football players’ value in the transfer market, Soft Computing 25(3) (2021), 2499-2511. [24] Y. W. Syaifudin and P. Puspitaningayu, Predicting winner of football match using analytical hierarchy process: an analysis based on previous matches data, In Int. Conf. on Data Analy. Bus. Industry, 2021, pp. 47-52. [25] M. Kleina, M. N. D. Santos, T. N. D. Santos, M. A. M. Marques and W. D. A. Silva, Artificial intelligence techniques applied to predict teams position of the Brazilian football championship, J. Physical Educ. 32(1) (2022), e3254. [26] Y. Geurkink, J. Boone, S. Verstockt and J. G. Bourgois, Machine learning-based identification of the strongest predictive variables of winning and losing in Belgian professional soccer, Appl. Sci. 11(5) (2021), Art. 2378. [27] E. Filiz, Evaluation of match results of five successful football clubs with ensemble learning algorithms, Res. Quart. Exer. Sport 2022. https://doi.org/10.1080/02701367.2022.2053647. [28] M. Muszaidi, A. B. Mustapha, S. Ismail and N. Razali, Deep Learning Approach for football match classification of English Premier League (EPL) based on full-time results, Springer Proc. Physics 273 (2022), 339-350. [29] R. Bunker and T. Susnjak, The application of machine learning techniques for predicting match results in team sport: a review, J. Artificial Intel. Res. 73 (2022), 1285-1322. [30] H. I. Okagbue, C. A. Nzeadibe and J. A. Teixeira da Silva, Predicting access mode of multidisciplinary and library and information sciences journals using machine learning, COLLNET J. Scientometrics Info. Manag. 16(1) (2022), 117-124. [31] H. I. Okagbue, E. M. Akhmetshin and J. A. Teixeira da Silva, Distinct clusters of cite score and percentiles in top 1000 journals in Scopus, COLLNET J. Scientometrics Info. Manag. 15(1) (2021), 133-143. [32] H. I. Okagbue, P. E. Oguntunde, P. I. Adamu and O. A. Adejumo, Unique clusters of patterns of breast cancer survivorship, Health Technol. 12(2) (2022), 365-384. [33] H. I. Okagbue, P. I. Adamu, P. E. Oguntunde, E. C. M. Obasi and O. A. Odetunmibi, Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer, Health Technol. 11(4) (2021), 887-893. [34] H. I. Okagbue, P. E. Oguntunde, E. C. M. Obasi, P. I. Adamu and A. A. Opanuga, Diagnosing malaria from some symptoms: a machine learning approach and public health implications, Health Technol. 11 (2021), 23-37. [35] C. O. Iroham, S. Misra, O. C. Emebo and H. I. Okagbue, Predictive rental values model for low-income earners in slums: the case of Ijora, Nigeria. Int. J. Constr. Manag. (2021). https://doi.org/10.1080/15623599.2021.1975021.
|