Predicting Outcomes in Video Games with Long Short Term Memory Networks

• URL: http://arxiv.org/abs/2402.15923v1
• Date: Sat, 24 Feb 2024 22:36:23 GMT
• Title: Predicting Outcomes in Video Games with Long Short Term Memory Networks
• Authors: Kittimate Chulajata, Sean Wu, Fabien Scalzo, Eun Sang Cha
• Abstract summary: Our work attempts to enhance audience engagement within video game tournaments by introducing a real-time method of predicting wins. As a proof of concept, we evaluate our model's performance within a classic, two-player arcade game, Super Street Fighter II Turbo.
• Score: 0.39723189359605243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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