Analyzing Player Performance Metrics for Rank Prediction in Valorant Using Random Forest: A Data-Driven Approach to Skill Profiling in the Metaverse - Rahardja, Untung; Aini, Qurotul (International Journal Research on Metaverse, 2025)
This study explores the application of Random Forest, a powerful data mining technique, to predict player ranks in Valorant, a competitive first-person shooter. By analyzing a range of player performance metrics, including headshots, kills, damage received, and traded kills, the study identifies the key features that influence player rank determination. Using a dataset of player statistics, the model was trained to predict player ranks, achieving a prediction accuracy of 50.09%. The analysis revealed that headshots and traded kills were the most influential metrics in determining player rank, suggesting that skill-based metrics like accuracy and tactical gameplay are crucial for ranking in the game. These findings highlight the importance of understanding the relationship between various performance indicators and rank progression, offering valuable insights for both game developers and players. The results contribute to the growing body of research in gaming analytics, showcasing how data mining techniques can be used to analyze player behavior and improve competitive balance in games. The study underscores the potential of using data-driven approaches to enhance game design, providing developers with actionable insights to refine rank prediction systems, adjust in-game mechanics, and ensure a more balanced competitive environment. Looking ahead, future research can explore the use of alternative machine learning models, such as support vector machines (SVM), XGBoost, or neural networks, to improve the prediction accuracy and robustness of the model. Additionally, expanding the dataset to include more detailed player behaviors, match outcomes, and even temporal aspects of player performance could provide a more comprehensive understanding of the factors influencing player ranks. This can help further unravel the complexities of player behavior and performance in the metaverse, where virtual environments evolve dynamically based on player interactions.