Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

TL;DR

This study introduces sensor data analysis combined with machine learning models to predict player outcomes in League of Legends, aiming to detect burnout or fatigue and improve performance analytics beyond in-game data limitations.

Contribution

The paper presents a novel approach using physiological sensor data and machine learning to predict game outcomes and detect player burnout, addressing the limitations of in-game data-based models.

Findings

Transformer model achieves ROC AUC 0.706 for 10-second forecast

Model predicts player loss in 88.3% of cases with 73.5% accuracy

Physiological features influence win/lose predictions

Abstract

Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on how to perform better. However, in-game mechanics and trends are frequently changed by new patches limiting the lifespan of the models trained exclusively on the in-game logs. In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter. The sensor data were collected from 10 participants in 22 matches in League of Legends video game. We have trained machine learning models including Transformer and Gated Recurrent Unit to predict whether the player wins the encounter taking place after some fixed time in the future. For 10 seconds forecasting horizon Transformer neural network architecture achieves ROC AUC score 0.706. This model is further developed into the detector capable of predicting that a player will lose the encounter occurring in 10 seconds in 88.3% of cases with 73.5% accuracy. This might be used as a players' burnout or fatigue detector, advising players to retreat. We have also investigated which physiological features affect the chance to win or lose the next in-game encounter.