Predicting Outcomes in Video Games with Long Short Term Memory Networks
<|reference_start|>Predicting Outcomes in Video Games with Long Short Term Memory Networks: 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.<|reference_end|>
@article{chulajata2024predicting,
    title={Predicting Outcomes in Video Games with Long Short Term Memory Networks},
    author={Kittimate Chulajata, Sean Wu, Fabien Scalzo, Eun Sang Cha},
    journal={arXiv preprint arXiv:2402.15923},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15923},
    primaryClass={cs.LG cs.AI cs.MM}
}