Real-time eSports Match Result Prediction
This addresses match outcome prediction for eSports enthusiasts and analysts, but it is incremental as it builds on existing methods with feature enhancements.
The paper tackles the problem of predicting winning teams in Dota 2 eSports matches by incorporating more prior features and real-time data, achieving up to 93.73% accuracy at the 40th minute.
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.