Win Prediction from the Snowball Effect Perspectives

Chanhyeok Jung, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The global E-sports market has been growing steadily. In particular, 'League of Legends' holds large international competitions every year, and professional leagues are held in each region. This paper conducted a study to predict advantageous teams in real-time using the time series data of League of Legends. A dataset was built by collecting game data with the API provided by Riot Games. Existing win-loss prediction studies using time series data have a limitation in that they learn as the final win-loss team without considering the flow of the game. To compensate for this, we propose a method of classifying advantageous real-time teams based on global gold indicators and learning with time series models. We trained LSTM, GRU, and RNN models using 76 features that subdivided the collected in-game data by position. As a result, our experiments show that all three models achieve an accuracy of more than 91 %.

Original languageEnglish
Title of host publication2022 IEEE Games, Entertainment, Media Conference, GEM 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665461382
Publication statusPublished - 2022
Event2022 IEEE Games, Entertainment, Media Conference, GEM 2022 - St. Michael, Barbados
Duration: 2022 Nov 272022 Nov 30

Publication series

Name2022 IEEE Games, Entertainment, Media Conference, GEM 2022


Conference2022 IEEE Games, Entertainment, Media Conference, GEM 2022
CitySt. Michael

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • E-sports
  • League of Legends
  • Recurrent Neural Networks
  • Snowball effect
  • Win prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Media Technology
  • Education


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