USE OF THE FIRST AND SECOND HALVES RESULTS TO CLASSIFY THE FINAL OUTCOME OF ENGLISH PREMIER LEAGUE MATCHES
Keywords:
adaptive boosting, chi-square test, English premier league, football, gradient boosting, linear regression, machine learning, random forests.DOI:
https://doi.org/10.17654/0972361722080Abstract
English premier league (EPL) is one of the top leagues in Europe and any analysis of data generated from the league is highly sought after by fans, betters, coach, managers and scouters. The paper applied four machine learning models in classifying the outcome of five seasons using the results of the first and the second halves. Each half and the final outcome were made up of just three data points, namely, home win (HW), draw (DR) and away win (AW). Home win is the most frequent followed by AW and DR in descending order. There is no significant relationship between the results of the two halves. On the other hand, there are significant relationships between the first half and the outcome and also, between the second half and the outcome. Random forests (RF), gradient boosting (GB) and adaptive boosting (AB) yielded better results than the logistic regression (LR). Generally, the accuracy averaged over 90 percent with few misclassifications. Implementation of the research in a decision support system is highly recommended.
Received: August 8, 2022
Accepted: September 15, 2022
References
P. Xenopoulos and C. Silva, Graph neural networks to predict sports outcomes, Proceedings, IEEE International Conference on Big Data, Big Data, 2021, pp. 1757-1763. https://doi.org/10.1109/BigData52589.2021.9671833.
E. Filiz, Evaluation of match results of five successful football clubs with ensemble learning algorithms, Research Quarterly for Exercise and Sport (2022). https://doi.org/10.1080/02701367.2022.2053647.
A. Ranjan, V. Kumar, D. Malhotra, R. Jain and P. Nagrath, Predicting the result of English premier league matches, Lecture Notes in Networks and Systems 203 (2021), 435-446. 10.1007/978-981-16-0733-2_30.
S. Jain, E. Tiwari and P. Sardar, Soccer result prediction using deep learning and neural networks, Lecture Notes on Data Engineering and Communications Technologies 57 (2021), 697-707. https://doi.org/10.1007/978-981-15-9509-7_57.
G. Boshnakov, T. Kharrat and I. G. McHale, A bivariate Weibull count model for forecasting association football scores, International Journal of Forecasting 33(2) (2017), 458-466. https://doi.org/10.1016/j.ijforecast.2016.11.006.
L. S. Benz and M. J. Lopez, Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression, AStA Advances in Statistical Analysis (2021).
https://doi.org/10.1007/s10182-021-00413-9.
T. Liu, A. Garcia-de-Alcaraz, H. Wang, P. Hu and Q. Chen, Impact of scoring first on match outcome in the Chinese Football Super League, Frontiers in Psychology 12 (2021), 662-708. https://doi.org/10.3389/fpsyg.2021.662708.
F. Liu, Y. Shi and L. Najjar, Application of design of experiment method for sports results prediction, Procedia Computer Science 122 (2017), 720-726. https://doi.org/10.1016/j.procs.2017.11.429.
N. Razali, A. Mustapha, N. Mustapha and F. M. Clemente, A Bayesian approach for major European football league match prediction, International Journal of Nonlinear Analysis and Applications 12 (2021), 971-980. https://doi.org/10.22075/IJNAA.2021.5544.
A. C. Constantinou, N. E. Fenton and M. Neil, Profiting from an inefficient association football gambling market: prediction, risk and uncertainty using Bayesian networks, Knowledge-Based Systems 50 (2013), 60-86. https://doi.org/10.1016/j.knosys.2013.05.008.
A. Joseph, N. E. Fenton and M. Neil, Predicting football results using Bayesian nets and other machine learning techniques, Knowledge-Based Systems 19(7) (2006), 544-553. https://doi.org/10.1016/j.knosys.2006.04.011.
R. Baboota and H. Kaur, Predictive analysis and modelling football results using machine learning approach for English Premier League, International Journal of Forecasting 35(2) (2019), 741-755. https://doi.org/10.1016/j.ijforecast.2018.01.003.
S. K. Andrews, K. L. Narayanan, K. Balasubadra and M. S. Josephine, Analysis on sports data match result prediction using machine learning libraries, Journal of Physics: Conference Series 1964(4) (2021), 042-085. https://doi.org/10.1088/1742-6596/1964/4/042085.
R. P. Bunker and F. Thabtah, A machine learning framework for sport result prediction, Applied Computing and Informatics 15(1) (2019), 27-33. https://doi.org/10.1016/j.aci.2017.09.005.
H. I. Okagbue, P. E. Oguntunde, P. I. Adamu and O. A. Adejumo, Unique clusters of patterns of breast cancer survivorship, Health and Technology 12(2) (2022), 365-384. https://doi.org/10.1007/s12553-021-00637-4.
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 and Technology 11(4) (2021), 887-893. https://doi.org/10.1007/s12553-021-00572-4.
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 and Technology 11 (2021), 23-37. https://doi.org/10.1007/s12553-020-00488-5.
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 Journal of Scientometrics and Information Management 16(1) (2022), 117-124. https://doi.org/10.1080/09737766.2021.2009745.
H. I. Okagbue, E. M. Akhmetshin and J. A. Teixeira da Silva, Distinct clusters of CiteScore and percentiles in top 1000 journals in Scopus, COLLNET Journal of Scientometrics and Information Management 15(1) (2021), 133-143. https://doi.org/10.1080/09737766.2021.1934604.
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, Journal of Physical Education 32(1) (2022), e3254. https://doi.org/10.4025/jphyseduc.v32i1.3254.
V. S. Arrul, P. Subramanian and R. Mafas, Predicting the football players’ market value using neural network model: a data-driven approach, IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE, 2022. https://doi.org/10.1109/ICDCECE53908.2022.9792681.
A. Majumdar, R. Bakirov, D. Hodges, S. Scott and T. Rees, Machine learning for understanding and predicting injuries in football, Sports Medicine-Open 8(1) (2022), Article 73. https://doi.org/10.1186/s40798-022-00465-4.
R. Bunker and T. Susnjak, The application of machine learning techniques for predicting match results in team sport: a review, Journal of Artificial Intelligence Research 73 (2022), 1285-1322. https://doi.org/10.1613/jair.1.13509.
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), 2378. https://doi.org/10.3390/app11052378.
U. Haruna, J. Z. Maitama, M. Mohammed and R. G. Raj, Predicting the outcomes of football matches using machine learning approach, Communications in Computer and Information Science 1547 (2022), 92-104. https://doi.org/10.1007/978-3-030-95630-1_7.
L. Carloni, A. De Angelis, G. Sansonetti and A. Micarelli, A machine learning approach to football match result prediction, Communications in Computer and Information Science 1420 (2021), 473-480. https://doi.org/10.1007/978-3-030-78642-7_63.
R. Nestoruk and G. Slowinski, Prediction of football games results, CEUR Workshop Proceedings 2951 (2021), 156-165.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Pushpa Publishing House, Prayagraj, India

This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 