Computer Vision for MOBA Analytics: A Dataset and Baseline for Visibility Analysis in Dota 2
For MOBA analytics researchers, this provides a new dataset and method to analyze team visibility, a previously underexplored aspect, though the approach is incremental.
This work introduces Dota2-Vis, a video-based dataset and baseline pipeline for visibility analysis in professional Dota 2 matches, using YOLO11l for player-icon detection to estimate opponent-visible player presence over time, revealing behavioral patterns not captured by structured data.
Introduction: Most Multiplayer Online Battle Arena (MOBA) analytics studies rely on structured data, which does not directly capture what each team could actually see during a match. Objective: This work introduces Dota2-Vis, a video-based dataset, and a baseline pipeline for visibility analysis in professional Dota 2 matches. Methodology: The dataset comprises all 144 matches from The International 2025, recorded from both team perspectives, totaling 288 Full HD videos, together with 2,477 manually annotated minimap images. We evaluate multiple variants of a modern object detector for player-icon detection and use the best-performing model to estimate opponent-visible player presence over time. Results: YOLO11l (large) achieved the best overall performance, reliably identifying player icons even in dense and visually cluttered minimap scenes. The resulting visibility curves reveal player, hero, role, and team-level patterns that complement conventional MOBA analytics, highlighting behavioral differences that are difficult to obtain from structured data alone. The dataset and code are publicly available at https://github.com/RicardoRCarvalho/dota2-vis/.