Predicting Outcomes in Video Games with Long Short Term Memory Networks
Kittimate Chulajata Sean Wu Fabien Scalzo Eun Sang Cha
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.