Reverse Engineering the League of Legends Game Engine for High-Fidelity Datasets

This article details the creation of a high-fidelity League of Legends dataset by reverse engineering the game engine and replay file format. Existing datasets and analytics tools suffer from low granularity, imprecision, and incompleteness. The author's tool captures precise player positions, ability usage timings, and damage calculations at millisecond intervals. The article describes the technical challenges, including decrypting internal replay files, emulating the game engine, and processing encrypted packets. This work has significant implications for reinforcement learning and offers valuable insights into data extraction for similar games.