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Implementation of user playstyle coaching using video processing and statistical methods in league of legends

Multimedia Tools and Applications volume 80, pages 34189–34201 (2021)Cite this article

Abstract

Recently, the game market is growing rapidly with the growth of e-sports. In particular, the market for League of Legends games continues to grow. Massively Online Battle Arena (MOBA) games have grown to become a massive industry, projected to reach over 140 billion USD worth of market value. Many users learn the League of Legends game, but their skills do not improve. Current analysis of the player’s manual playback through visual media such as video is the most common method. Therefore, this paper extracts data from gameplay videos and analyzes intuitive gameplay “styles” in the popular MMO game League of Legends to provide coaching-specific information. we were able to classify whether the player is cooperative and aggressive, but if additional information which we did not extract nor process like map vision were taken into account, Big Data and Machine Learning could come into play.

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Authors and Affiliations

  1. Boin High School, 18 Ogeum-ro 49-gil, Ogeum-dong, Songpa-gu, Seoul, South Korea

    Shinyoung Kim

  2. PlayAuto Inc., 18 Ogeum-ro 49-gil, Ogeum-dong, Songpa-gu, Seoul, South Korea

    Dohyeon Kim

  3. Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Myeongnyun 3(sam)ga-dong, Jongno-gu, Seoul, South Korea

    HyungGeun Ahn

  4. Liberal & Arts College, Anyang University, Anyang, South Korea

    Byeongtae Ahn

Authors
  1. Shinyoung Kim
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  2. Dohyeon Kim
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  3. HyungGeun Ahn
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  4. Byeongtae Ahn
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Correspondence to Byeongtae Ahn.

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Kim, S., Kim, D., Ahn, H. et al. Implementation of user playstyle coaching using video processing and statistical methods in league of legends. Multimed Tools Appl 80, 34189–34201 (2021). https://doi.org/10.1007/s11042-020-09413-4

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  • DOI: https://doi.org/10.1007/s11042-020-09413-4

Keywords

  • LoL game
  • Video processing
  • Coaching education
  • Deep learning
  • Playstyle