Real-time eSports Match Result Prediction

2016/12/10 by Yang, Yifan, Qin, Tian, Lei, Yu-Heng · 1 citation
#Applications (stat.AP) #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #I.2.1 #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.1701.03162

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.

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