Recommendation System for Defining Competitive Teams in eSports: Experimental Analysis of CBLOL - Zecca, Vinícius Antonio Ramos; Contreras, Rodrigo Colnago (Anais Estendidos do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames), 2025)
Roster building in esports is a complex strategic challenge that can be optimized using data-driven methodologies. This study proposes a novel approach for assembling League of Legends teams in the CBLOL, integrating performance analysis and machine learning techniques. The method accounts for role-specific performance metrics and introduces a network-based metric inspired by the Erdős Number to assess a player’s competitive experience. The core objective is to build a recommendation system that supports roster selection by identifying players likely to contribute to team success. Case studies show the methodology’s application in team selection, When predicting team performance in the CBLOL 2024 Split 2, the model yielded a Pearson correlation of 0.89 between predicted and actual rankings, highlighting its effectiveness in competitive roster building.