用游戏录像中的战术特征预测VALORANT回合胜负，准确率超81%。
Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis
- 从游戏录像的迷你地图中提取角色位置等战术信息
- 融合战术标签的数据集使预测准确率达81%以上
- 适合对电竞策略分析和视频理解感兴趣的读者
近期关于电子竞技比赛结果预测的研究多基于比赛日志与统计数据，但本研究聚焦于需要复杂策略的FPS游戏VALORANT，通过分析比赛录像中的迷你地图信息，构建回合结果预测模型。基于TimeSformer视频识别模型，我们引入了角色位置等详细战术特征，以及游戏中其他事件信息。实验表明，使用包含这些战术事件标签的数据集训练的模型，在回合中段及之后阶段的预测准确率约为81%，显著优于仅使用原始迷你地图信息训练的模型。结果表明，从比赛录像中提取战术特征对预测VALORANT回合结果具有显著效果。
原文摘要 · Abstract (English)
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.
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