Mining PUBG Winning Rates: Between Chicken Dinnerand In Game Traveling Distance

Abstract

This research article explores the relationship between travel distance and player performance in PlayerUnknown's Battlegrounds (PUBG), a popular battle royale game. Focusing on walking, riding, and swimming distances, our study employs machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), to predict PUBG player performance, with a primary emphasis on solo matches. Our research encompasses data collection, filtering, model configuration, data sampling, and model evaluation stages to ensure data reliability and model appropriateness. The results highlight the consistent superiority of the Random Forest model, which demonstrates the lowest Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the highest R-squared (R2) values in both training and testing phases. In contrast, the SVM-Sigmoid model consistently delivers poor predictive performance, emphasizing the significance of selecting an appropriate model. In summary, this research unveils the vital role of travel distance in PUBG player performance and underscores the importance of data-driven decision-making and model selection in achieving accurate and reliable predictions. This contribution enhances our understanding of player performance in the dynamic world of PUBG.