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http://dx.doi.org/10.3745/KTSDE.2020.9.2.61

Predicting Win-Loss of League of Legends Using Bidirectional LSTM Embedding  

Kim, Cheolgi (숭실대학교 융합소프트웨어학과)
Lee, Soowon (숭실대학교 소프트웨어학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.9, no.2, 2020 , pp. 61-68 More about this Journal
Abstract
E-sports has grown steadily in recent years and has become a popular sport in the world. In this paper, we propose a win-loss prediction model of League of Legends at the start of the game. In League of Legends, the combination of a champion statistics of the team that is made through each player's selection affects the win-loss of the game. The proposed model is a deep learning model based on Bidirectional LSTM embedding which considers a combination of champion statistics for each team without any domain knowledge. Compared with other prediction models, the highest prediction accuracy of 58.07% was evaluated in the proposed model considering a combination of champion statistics for each team.
Keywords
League of Legends; Win-Loss Prediction; Machine Learning; Neural Network; LSTM;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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