Winning Probability Prediction in the FIFA ASEAN Cup 2026 Premier Division Using Naïve Bayes
Year: 2026
Type: article
Abstract: This study aims to predict the championship probability of teams participating in the FIFA ASEAN Cup 2026 Premier Division using the Gaussian Naïve Bayes algorithm. The prediction model was developed based on five performance indicators, namely FIFA Ranking, Win Rate, Average Goals Scored, Average Goals Conceded, and Goal Difference. Historical data from the 2020, 2022, and 2024 ASEAN Championship editions were used to construct the classification model, with teams classified into two categories: Champion and Non-Champion. The historical dataset was divided into training and testing sets using an 80:20 stratified sampling approach to maintain the proportion of the two target classes. The Gaussian Naïve Bayes model was trained using the selected performance indicators and subsequently evaluated using Accuracy, Precision, Recall, and F1-score. The results show that the model achieved an Accuracy of 100.00%, Precision of 100.00%, Recall of 100.00%, and F1-score of 100.00% on the testing dataset. The trained model was then applied to the performance data of the FIFA ASEAN Cup 2026 Premier Division teams to estimate their championship probabilities. The prediction results identified Indonesia as the leading championship candidate with an estimated probability of 78.79%, followed by Vietnam at 8.42% and Thailand at 6.31%. Malaysia, the Philippines, Singapore, Bangladesh, and Pakistan obtained lower estimated probabilities. These findings demonstrate that Gaussian Naïve Bayes can provide a quantitative framework for estimating and comparing the relative championship potential of ASEAN football teams based on historical performance indicators.
Source: Perfect.
Author: M. Iqbal Ulumando
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7225561375