Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
This work provides a novel approach for detecting player burnout or fatigue using sensor data, offering a tool for eSports players and coaches to improve practice and performance by advising when to rest.
This paper proposes using sensor data to predict video game player burnout, which is framed as predicting future match loss. A Transformer model achieved a ROC AUC score of 0.706 for predicting loss within a 10-second horizon, and could detect future losses in 88.3% of cases with 73.5% accuracy.
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.