Mobile Legends Win Rate Prediction and Team Recommendation Using Switched Hero Roles
Journal Title: International Journal of Innovations in Science and Technology - Year 2025, Vol 7, Issue 1
Abstract
Introduction: Mobile Legends Bang Bang (MLBB) falls under the category of a multi-line battle arena game which requires players to have strong skills and strategic gameplay; team composition is an important factor influencing the chances of winning the game. Novelty Statement: Although there is data currently available for MLBB, two aspects of this game that remain unexplored include: i) win rate prediction using nontraditional roles in heroes, and ii) team composition with switched hero roles. Material and Method: This research aims to address this issue by predicting the win rate of heroes with switched roles. This unpredictability will lead to the formation of a team that can have a significant advantage over the enemy team thus leading to victory. The dataset for this study was formulated focusing on 67 heroes in the game. The win rates were generated with real-time simulations where the ally team members remained unchanged to avoid biased results. Result and Discussion: The research utilized two model-building approaches and win rate predictions were made using 12 regression algorithms under 5 feature selection settings. The results show that LightGBM with AdaBoost as the base estimator provides better results and was used to formulate 5 teams. A recommendation system was designed to optimize team composition from the win rate prediction analysis. To validate the results, we simulated 50 matches with each team resulting in a 94% win rate. Concluding Remarks: The research explores switched hero roles and provides promising results to help team formation with an increased chance of victory when using non-traditional hero roles.
Authors and Affiliations
Pir Hamid Ali Qureshi, Areej Fatemah Meghji, Rabeea Jaffari
Keywords
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