# Predicting Outcomes in Video Games with Long Short Term Memory Networks

arXiv: https://arxiv.org/abs/2402.15923 · HTML: https://mlsanity.com/paper/2402.15923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 19 of 19)

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- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Exploring the Limits of Language Modeling](https://mlsanity.com/paper/1602.02410/) (2016)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Language Models with Transformers](https://mlsanity.com/paper/1904.09408/) (2019)
- [Efficient Classification of Long Documents Using Transformers](https://mlsanity.com/paper/2203.11258/) (2022)
- [Image Captioning with Deep Bidirectional LSTMs](https://mlsanity.com/paper/1604.00790/) (2016)
- [A Generative Model for Raw Audio Using Transformer Architectures](https://mlsanity.com/paper/2106.16036/) (2021)
- [Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.03821/) (2019)
- [Transformers: "The End of History" for NLP?](https://mlsanity.com/paper/2105.00813/) (2021)
- [Real-time eSports Match Result Prediction](https://mlsanity.com/paper/1701.03162/) (2016)
- [Transformer-based end-to-end classification of variable-length volumetric data](https://mlsanity.com/paper/2307.06666/) (2023)

## Abstract (arXiv)

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.