Machine Learning-Based Win Prediction in Mobile Legends

I developed a machine learning-based prediction system to estimate team win probability in Mobile Legends: Bang Bang, a popular MOBA mobile game. The system utilized the Naive Bayes algorithm to classify and calculate the winning likelihood of a team based on hero composition and several critical factors such as role synergy and build strategies.

Tools: MATLAB

Algorithms: Naive Bayes

• Help players understand win potential based on their team’s composition. • Provide a decision support system for better hero selection and strategy planning. • Highlight the importance of teamwork, hero mastery, and item builds in ranked matches.

• Collected and labeled gameplay data from 30 real matches. • Identified key loss factors: poor team composition, incorrect item builds, and lack of hero mastery. • Applied the Naive Bayes classification algorithm to calculate win probabilities. • Created a user interface (as a prototype) where users input team composition and receive win chance predictions. • Modeled user flow using Use Case Diagrams to capture system requirements.

• Built a lightweight and educational prediction tool that served as a guide for strategic gameplay. • Demonstrated how machine learning could enhance user decision-making in competitive games. • The project received strong academic feedback and showcased the applicability of data science in game environments.

• Limited dataset (30 matches) required thoughtful data preparation and assumption handling. • Translating complex in-game variables into measurable and meaningful inputs for the model.

• Complete dataset and preprocessing scripts. • Functional prototype of the prediction system. • Final report and academic presentation.

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