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All Journal Formosa Journal of Computer and Information Science
Apriliano Ernando Benyamin Boimau
Universitas Amikom Yogyakarta
Computer Science & IT

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Quantitative Analysis of Player Win Rate Prediction in Magic Chess Using the Random Forest and XGBoost Algorithms Apriliano Ernando Benyamin Boimau; Nafiatun Sholihah; Ferian Fauzi Abdulloh; Majid Rahardi; Afrig Aminuddin
Formosa Journal of Computer and Information Science Vol. 5 No. 2 (2026): August 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i2.17145

Abstract

This condition makes players rely on intuition and choose speculative tactics without a measurable basis. This study applied quantitative analysis to predict the probability of winning by comparing Random Forest and XGBoost. A total of 517 match data for season 4 was collected semi-manually, then through cleaning, binary class labeling, encoding, and 80:20 training and test data sharing. The model was optimized using Grid Search and evaluated in 30 iterations based on Accuracy, Precision, Recall, and F1-Score. The results show that Random Forest is optimal and stable, with an Accuracy of 53.27% and a Precision of 57.12%. Feature Importance shows that Commander and Go-Go Commander are the most dominant in winning over synergy compositions, and are implemented in web prototypes.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Majid Rahardi Nafiatun Sholihah