A Study on the Factors Influencing Rank Prediction in PlayerUnknown’s Battlegrounds

Electronics, Journal Year: 2025, Volume and Issue: 14(3), P. 626 - 626

Published: Feb. 5, 2025

This study analyzes the key factors influencing player rank prediction in PlayerUnknown’s Battlegrounds (PUBG), using machine learning models to evaluate in-game performance. By examining variables such as “walkDistance”, “boosts”, and “weaponsAcquired”, identifies these critical predictors, with “walkDistance” emerging most significant across all match types. Utilizing including random forest (RF), gradient descent (GD), extreme boosting (XGBoost), feedforward neural network (FNN), analysis reveals performance variation by type: XGBoost achieves highest accuracy solo matches (88.07%), GD performs best duo (84.75%), RF records squad (78.21%). These findings provide valuable insights for game developers balancing gameplay offer personalized strategic recommendations players. Future research may enhance predictive incorporating additional exploring alternative applicable PUBG similar battle royale games.

Language: Английский

Bias evaluation and minimization for estuarine total dissolved solids (TDS) patterns constructed using spatial interpolation techniques

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 210, P. 117353 - 117353

Published: Nov. 29, 2024

Language: Английский