Trajectory Analysis and Prediction in Counter-Strike with LSTM Models

This paper explores the use of Long-Short Term Memory Neural Networks (LSTM-NNs) for analyzing and predicting player trajectories. Utilizing the ESTA dataset, which includes data from professional Counter-Strike matches, the study aims to uncover patterns in player movements and evaluate the accuracy of LSTM-NNs in predicting trajectories. Through systematic experimentation, the model achieved a test Mean Absolute Error (MAE) of 14.44 and Mean Squared Error (MSE) of 477.24, demonstrating strong generalization and alignment between predicted and actual trajectories. The paper outlines a straightforward methodology and experimental setup that can be readily applied to other game genres where player trajectory analysis can offer valuable insights. These findings have significant implications for enhancing player performance, developing advanced game strategies, and improving the overall gaming experience.