Forecasting winners in E-sports with real-time analytics has the potential to
further engage audiences watching major tournament events. However, making such
real-time predictions is challenging due to unpredictable variables within the
game involving diverse player strategies and decision-making. Our work attempts
to enhance audience engagement within video game tournaments by introducing a
real-time method of predicting wins. Our Long Short Term Memory Network (LSTMs)
based approach enables efficient predictions of win-lose outcomes by only using
the health indicator of each player as a time series. As a proof of concept, we
evaluate our model's performance within a classic, two-player arcade game,
Super Street Fighter II Turbo. We also benchmark our method against state of
the art methods for time series forecasting; i.e. Transformer models found in
large language models (LLMs). Finally, we open-source our data set and code in
hopes of furthering work in predictive analysis for arcade games.

我们的研究旨在通过引入实时方法预测获胜来增强电子竞技比赛的观众参与度。我们的基于长短期记忆网络（LSTM）的方法通过仅使用每个玩家的健康指标作为时间序列，实现了高效的胜负结果预测。我们以经典的两人对战街头霸王 II Turbo 为例，评估了我们模型在此游戏中的表现，并与流行的时间序列预测方法进行了对比。最后，我们公开了数据集和代码，以期促进对街机游戏的预测性分析进一步的研究。

使用长短期记忆网络预测电子游戏结果

Predicting Outcomes in Video Games with Long Short Term Memory Networks

Billions of live-streaming viewers share their opinions on scenes they are
watching in real-time and interact with the event, commentators as well as
other viewers via text comments. Thus, there is necessary to explore viewers'
comments with scenes in E-sport live-streaming events. In this paper, we
developed CS-lol, a new large-scale dataset containing comments from viewers
paired with descriptions of game scenes in E-sports live-streaming. Moreover,
we propose a task, namely viewer comment retrieval, to retrieve the viewer
comments for the scene of the live-streaming event. Results on a series of
baseline retrieval methods derived from typical IR evaluation methods show our
task as a challenging task. Finally, we release CS-lol and baseline
implementation to the research community as a resource.

本研究通过创建一个包含游戏场景和观众评论的大型数据集 CS-lol，来探索电竞直播中观众评论和场景的关系，提出了难度较高的观众评论检索任务，并在基线检索方法中取得了优异的成果。