Enabling Real-Time Prediction of In-game Deaths through Telemetry in Counter-Strike: Global Offensive

Abstract

Esports have evolved into a major form of entertainment, drawing hundreds of millions of viewers to its online competitive broadcasts. Using Esports telemetry data to predict the outcome of a match is a well-researched topic, but micropredictions of specific in-game events are explored only sparingly. How accurately can we predict specific in-game events within a limited time window, and how can these predictions be used in a live broadcast? This research aims at predicting in-game deaths using telemetry data in Counter-Strike: Global offensive (CS:GO). We establish a data processing pipeline to acquire and re-structure raw in-game data and propose a set 36 features which will ultimately be used to predict in-game deaths within a three second window. Three neural network models are compared, namely convolutional (CNN), recurrent (RNN) and long short-term memory (LSTM). Our results show that the LSTM network has the best predictive accuracy (F1 0.38) when prompted, for all 10 players of a competitive game of CS:GO. The predictions are most influenced by features related to a player’s average in-game death count, health points, enemies in range and equipment value. Our model enables real-time micropredictions of deaths in CS:GO, and may be leveraged by Esports commentators and game observers to direct their focus on critical in-game events during a live competitive broadcast.