MLBB Heroes Matchup Decision Support System

My project is called MLBB Hero Matchup Decision Support System, a SwiftUI application designed to help casual Mobile Legends players make better hero-picking decisions. Many players rely on intuition when choosing heroes, so I wanted to provide a more objective and structured way to understand hero matchups. The system uses a rule-based scoring engine built in Swift, which evaluates heroes through three main factors: playstyle, traits, and weaknesses. Playstyles such as teamfight, pickoff, and split-push have counter relationships, while traits and weaknesses are matched to identify how effectively one hero can exploit another. The data comes from locally stored JSON files, online references, and insights from experienced players. All calculations are performed locally, making the application fast and independent of external servers. When a user selects a hero, the app generates the Top 5 recommended counter heroes along with their scores. Each score is calculated through a normalized scoring process to ensure fairness and avoid double counting, making the recommendations both transparent and explainable. The goal of this project is to simplify complex matchup knowledge and help casual players make more informed decisions while playing Mobile Legends.