Sensors and Game Synchronization for Data Analysis in eSports
This addresses the need for better training tools in the professional eSports industry, though it is incremental as it focuses on data synchronization rather than novel analysis or broader ML/AI advancements.
The authors tackled the problem of improving eSports athlete training by developing a system that collects and synchronizes heterogeneous data (physiological, environmental, video, telemetry) with high accuracy, achieving up to 3 ms synchronization accuracy for gaming computers in CS:GO.
eSports industry has greatly progressed within the last decade in terms of audience and fund rising, broadcasting, networking and hardware. Since the number and quality of professional team has evolved too, there is a reasonable need in improving skills and training process of professional eSports athletes. In this work, we demonstrate a system able to collect heterogeneous data (physiological, environmental, video, telemetry) and guarantying synchronization with 10 ms accuracy. In particular, we demonstrate how to synchronize various sensors and ensure post synchronization, i.e. logged video, a so-called demo file, with the sensors data. Our experimental results achieved on the CS:GO game discipline show up to 3 ms accuracy of the time synchronization of the gaming computer.