Explainable e-sports win prediction through Machine Learning classification in streaming

TL;DR

This paper presents an explainable machine learning system for real-time e-sports win prediction using streaming data, achieving over 90% accuracy and enhancing decision-making with interpretability.

Contribution

It introduces a novel streaming classification approach with explainability for e-sports win prediction, surpassing existing batch methods in accuracy.

Findings

Achieved over 90% accuracy in win prediction

Outperformed existing solutions in literature

Enhanced trust through explainability module

Abstract

The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry. Even though Artificial Intelligence-based solutions for e-sports analytics are traditionally defined as extracting meaningful patterns from related data and visualizing them to enhance decision-making, most of the effort in professional winning prediction has been focused on the classification aspect from a batch perspective, also leaving aside the visualization techniques. Consequently, this work contributes to an explainable win prediction classification solution in streaming in which input data is controlled over several sliding windows to reflect relevant game changes. Experimental results attained an accuracy higher than 90 %, surpassing the performance of competing solutions in the literature. Ultimately, our system can be leveraged by ranking and recommender systems for informed decision-making, thanks to the explainability module, which fosters trust in the outcome predictions.