Smerdov Anton, Somov Andrey, Burnaev Evgeny, Stepanov Anton
CDE, Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia.
Multimed Tools Appl. 2023;82(7):11021-11046. doi: 10.1007/s11042-022-13464-0. Epub 2022 Aug 23.
The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated an attention mechanism improves the generalization of the network and provides a straightforward feature importance as well. The best model achieves Area Under the Receiver Operating Characteristic Curve (ROC AUC) score 0.73 in predicting whether a player will perform better or worse in the next 240 seconds based on in-game metrics. The prediction of the performance of a particular player is realized although their data are not utilized in the training set. The proposed solution has a number of promising applications for professional eSports teams and amateur players, such as a learning tool or performance monitoring system.
电子游戏和电子竞技的新兴发展缺乏用于确保专业和业余电子竞技团队进行高质量分析和训练的工具。我们报告了一种基于人工智能（AI）的解决方案，该方案仅使用传感器数据来预测电子竞技玩家的游戏内表现。为此，我们收集了专业和业余玩家的生理数据、环境数据以及智能椅子数据。使用循环神经网络从多人游戏的游戏日志中评估每个时刻的玩家表现。我们研究了一种注意力机制，它可以提高网络的泛化能力，并提供直接的特征重要性。最佳模型在根据游戏内指标预测玩家在接下来240秒内表现是更好还是更差时，实现了受试者工作特征曲线下面积（ROC AUC）得分0.73。尽管特定玩家的数据未在训练集中使用，但仍实现了对其表现的预测。所提出的解决方案对专业电子竞技团队和业余玩家有许多有前景的应用，例如学习工具或性能监测系统。
