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A Bayesian approach for major European football league match prediction | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 971-980 اصل مقاله (430.53 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5544 | ||
| نویسندگان | ||
| Nazim Razali1؛ Aida Mustapha* 2؛ Norwati Mustapha3؛ Filipe M Clemente4 | ||
| 1Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia | ||
| 2Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Malaysia | ||
| 3Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia | ||
| 4School of Sport and Leisure, Viana do Castelo Polytechnic Institute, Portugal | ||
| تاریخ دریافت: 11 خرداد 1400، تاریخ بازنگری: 19 شهریور 1400، تاریخ پذیرش: 02 مهر 1400 | ||
| چکیده | ||
| This paper presents a Bayesian Approach for Major European Football League match prediction. In this study, four variants of Bayesian approaches are investigated to observe the impact of different structural learning algorithms within the family of Bayesian Network which are Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and two General Bayesian Networks (GBN); K2 algorithm with BDeu scoring function (GBN-K2) and Hill Climbing algorithm with MDL scoring function (GBNHC). The predictive performance of all Bayesian approaches is evaluated and compared based on football match results from five major European Football League consisting of three complete seasons of 1,140 matches. The results showed that GBN-HC gained 92.01% of accuracy while GBN-K2 and TAN produced comparable results with 91.86% and 91.94% accuracy, respectively. The lowest result was produced by NB, with only 72.78% accuracy. The results suggest that TAN requires further exploration in football prediction with its ability to cater the minimal dependency among attributes in a small-sized dataset. | ||
| کلیدواژه‌ها | ||
| Football؛ Bayesian networks؛ Naive bayes؛ Tree augmented naive bayes and General bayesian networks | ||
| مراجع | ||
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