Serious games, professional entertainment (e.g. e-sport), or the immersive simulation of critical scenarios by means of virtual reality belong to the scope of the video game industry. With implications in economic (gambling) and professional careers (e-sports), researchers have focused on the high-level analysis of the win/lose chances and on the player’s profile (good/bad performers). At the low-level analysis, the prediction of player’s performance at single-action level, such as in the case of hits in a first-person shooter (FPS) video game, to the best of our knowledge, has not been undertaken yet. In this study we hypothesize that VR video games, embed enough contextual information to predict performance in an FPS game at single-action level. For this purpose, we developed an FPS video game and a single-shoot level prediction model based on virtual world contextual information. Eighteen students of the University of Granada without previous experience in the game played for 45–50 min and generated 600–1200 events each. Every event, which was composed of twenty-contextual-player-centred components of the virtual scenario, were transmitted on-line to a remote server to perform predictions. Data from fifteen out of eighteen participants were used to train the model prediction model. After training, the model predicted “hit”/”miss” with a mean accuracy of 74.1%. In a broad vision, our results suggest that immersive virtual environments bear enough contextual information for accurate predictions even at single-action level. In a closed-loop design, this finding could be used (e.g. in defence, professional e-sports, etc.) to anticipate participant’s actions/decisions before they are taken, and modify the virtual scenario (e.g. abort mission, change environmental conditions) or drive the player (e. g. suggest options, relieve of command, etc.) according to the purpose of the mission.

严肃的游戏，专业娱乐（例如，电子竞技）或通过虚拟现实对关键场景进行沉浸式仿真属于视频游戏行业。由于对经济（赌博）和职业（电子竞技）产生了影响，研究人员专注于对获胜/失败机会以及球员的个人资料（表现出色/不良）的高级分析。在低级分析中，就我们所知，还没有对玩家在单个动作级别的表现进行预测，例如在第一人称射击游戏（FPS）中被击中的情况。在本研究中，我们假设VR视频游戏嵌入了足够的上下文信息，以预测FPS游戏在单操作级别的性能。以此目的，我们开发了FPS视频游戏和基于虚拟世界上下文信息的单拍水平预测模型。格拉纳达大学（University of Granada）的18名没有游戏经验的学生玩了45–50分钟，每人产生了600–1200个事件。由虚拟场景的二十个以玩家为背景的组件组成的每个事件都在线传输到远程服务器以执行预测。来自18个参与者中的15个的数据用于训练模型预测模型。训练后，该模型预测“命中” /“未命中”，平均准确率为74.1％。从广义上讲，我们的结果表明，即使在单个动作级别，沉浸式虚拟环境也具有足够的上下文信息，可以进行准确的预测。在闭环设计中，可以使用此发现（例如，在防御，