Mining PUBG Winning Rates: Between Chicken Dinner and 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 Rsquared (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.