Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis

TL;DR

This paper develops a model to predict VALORANT round outcomes by analyzing tactical features from match footage, achieving around 81% accuracy, and demonstrating the effectiveness of tactical feature extraction over raw minimap data.

Contribution

It introduces a novel approach using tactical features from video analysis with TimeSformer to improve prediction accuracy in VALORANT.

Findings

Achieved approximately 81% prediction accuracy.

Tactical features significantly outperform raw minimap data.

Middle round phases yield higher prediction accuracy.

Abstract

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.