# Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis

arXiv: https://arxiv.org/abs/2510.17199 · HTML: https://mlsanity.com/paper/2510.17199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Real-time eSports Match Result Prediction](https://mlsanity.com/paper/1701.03162/) (2016)
- [Valuing Player Actions in Counter-Strike: Global Offensive](https://mlsanity.com/paper/2011.01324/) (2020)
- [ESTA: An Esports Trajectory and Action Dataset](https://mlsanity.com/paper/2209.09861/) (2022)
- [League of Legends: Real-Time Result Prediction](https://mlsanity.com/paper/2309.02449/) (2023)

## Abstract (arXiv)

Recently, research on predicting match outcomes in esports has been actively conducted, but much of it is based on match log data and statistical information. This research targets the FPS game VALORANT, which requires complex strategies, and aims to build a round outcome prediction model by analyzing minimap information in match footage. Specifically, based on the video recognition model TimeSformer, we attempt to improve prediction accuracy by incorporating detailed tactical features extracted from minimap information, such as character position information and other in-game events. This paper reports preliminary results showing that a model trained on a dataset augmented with such tactical event labels achieved approximately 81% prediction accuracy, especially from the middle phases of a round onward, significantly outperforming a model trained on a dataset with the minimap information itself. This suggests that leveraging tactical features from match footage is highly effective for predicting round outcomes in VALORANT.