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


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 2, pp. 61-68, Feb. 2020
https://doi.org/10.3745/KTSDE.2020.9.2.61,   PDF Download:
Keywords: League of Legends, Win-Loss Prediction, Machine Learning, Neural Network, LSTM
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.


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Cite this article
[IEEE Style]
C. Kim and S. Lee, "Predicting Win-Loss of League of Legends Using Bidirectional LSTM Embedding," KIPS Transactions on Software and Data Engineering, vol. 9, no. 2, pp. 61-68, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.2.61.

[ACM Style]
Cheolgi Kim and Soowon Lee. 2020. Predicting Win-Loss of League of Legends Using Bidirectional LSTM Embedding. KIPS Transactions on Software and Data Engineering, 9, 2, (2020), 61-68. DOI: https://doi.org/10.3745/KTSDE.2020.9.2.61.