Predicting Outcomes in Video Games with Long Short Term Memory Networks

arXiv:2402.15923v1 Announce Type: new
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 …

abstract analytics arxiv audience cs.ai cs.lg cs.mm decision diverse events forecasting game games major making memory networks predictions real-time sports strategies type variables video video games work