# Computer Vision for MOBA Analytics: A Dataset and Baseline for Visibility Analysis in Dota 2

arXiv: https://arxiv.org/abs/2606.26970 · HTML: https://mlsanity.com/paper/2606.26970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Do We Train on Test Data? Purging CIFAR of Near-Duplicates](https://mlsanity.com/paper/1902.00423/) (2019)
- [Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation](https://mlsanity.com/paper/2504.19347/) (2025)
- [Time to Die: Death Prediction in Dota 2 using Deep Learning](https://mlsanity.com/paper/1906.03939/) (2019)
- [Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation](https://mlsanity.com/paper/2012.09424/) (2020)
- [Esports Training, Periodization, and Software -- a Scoping Review](https://mlsanity.com/paper/2409.19180/) (2024)

## Built on by (most cited first, 1 of 1)

- [Esports World Model Dataset](https://mlsanity.com/paper/2610.16166/) (2026)

## Abstract (arXiv)

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/.