Win Prediction in Multiplayer Esports: Live Professional Match Prediction

Victoria J. Hodge, Sam Devlin, Nick Sephton, Florian Block, Peter I. Cowling, Anders Drachen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review


Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasized prematch prediction and used data from amateur games, which are more easily available than those from professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here, we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multiplayer online battle arena (MOBA) esport. This article evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games, and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85% accurate after 5 min of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities and practical challenges, and propose a set of evaluation criteria for research on esports win prediction.

TidsskriftIEEE Transactions on Games
Udgave nummer4
Sider (fra-til)368-379
StatusUdgivet - 2019


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