Real-time eSports Match Result Prediction

TL;DR

This paper improves eSports match outcome prediction by integrating pre-match player history and real-time in-game data, achieving significant accuracy gains with various models.

Contribution

It introduces a comprehensive feature set and models for real-time prediction, enhancing accuracy over previous approaches.

Findings

Adding more prior features increases accuracy from 58.69% to 71.49%.

Real-time features boost prediction accuracy to 93.73% at 40 minutes.

The Attribute Sequence Model contributes to improved prediction performance.

Abstract

In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contain replay data, our experiments show that adding more aspects of prior features improves accuracy from 58.69% to 71.49%, and introducing real-time features achieves up to 93.73% accuracy when predicting at the 40th minute.