AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

TL;DR

This paper presents an AI-based system that predicts eSports player performance using sensor data, enhancing training and analytics for both professional and amateur players.

Contribution

It introduces a novel AI approach utilizing heterogeneous sensor data and attention mechanisms to predict player performance in real-time.

Findings

Achieved ROC AUC score of 0.73 in performance prediction.

Demonstrated that attention mechanisms improve model generalization.

Predicted individual player performance without using their training data.

Abstract

The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the game chair data from Pro and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated that attention mechanism improves the generalization of the network and provides the straightforward feature importance as well. The best model achieves ROC AUC score 0.73. The prediction of the performance of particular player is realized although his data are not utilized in the training set. The proposed solution has a number of promising applications for Pro eSports teams and amateur players, such as a learning tool or a performance monitoring system.