Komparasi Algoritma Machine Learning pada Prediksi Kemenangan Tim VALORANT Berbasis Delta Analysis
- Bintang Ilham Kurniawan Universitas Dian Nuswantoro, Semarang, Indonesia
-
Sri Winarno
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
(*) Corresponding Author
Abstract
The development of the Valorant esports ecosystem through the VALORANT Champions Tour (VCT) creates a need for objective data analysis. Current analysis is often trapped in subjective bias, risking inaccurate predictions. To address this problem, this research compares various team win prediction methods in VCT 2025. The selection of the VCT 2025 dataset provides novelty as it represents a franchise league structure with much more evenly distributed game meta and tactical complexity than previous seasons. The compared algorithms include Naïve Bayes, Decision Tree, XGBoost, CatBoost, and Stacking Classifier. The main contribution of this research is the application of a differential feature extraction technique (Delta Analysis) to objectively measure the performance gap between teams. Comparative experimental results on 1,507 match observations show that the Stacking Classifier achieved the most optimal accuracy (75.72%) compared to XGBoost (75.19%), Naïve Bayes (74.26%), CatBoost (73.59%), and Decision Tree (72.86%). These findings prove that Delta Analysis-based meta-learning synergy can provide more stable analytical generalization in handling modern esports data complexity.
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