Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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Predicting Game Results using Machine Learning and Deriving Strategic Direction from Variable Importance

기계학습을 활용한 게임승패 예측 및 변수중요도 산출을 통한 전략방향 도출

  • Kim, Yongwoo (Graduate School of Technology & Innovation Management, Hanyang University) ;
  • Kim, Young‐Min (Graduate School of Technology & Innovation Management, Hanyang University)
  • 김용우 (한양대학교 기술경영대학원) ;
  • 김영민 (한양대학교 기술경영대학원)
  • Received : 2021.05.10
  • Accepted : 2021.06.17
  • Published : 2021.08.20
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Abstract

In this study, models for predicting the final result of League of Legends game were constructed for each rank using data from the first 10 minutes of the game. Variable importance was extracted from the prediction models to derive strategic direction in early phase of the game. As a result, it was possible to predict final results with over 70% accuracy in all ranks. It was found that early game advantage tends to lead to the final win and this tendency appeared stronger as it goes to challenger ranks. Kill(death) was found to be the most influential factor for win, however, there were also variables whose importance rank changed according to rank. This indicates there is a difference in the strategic direction in the early stage of the game depending on the rank.

본 연구에서는 게임 초반 10분의 데이터를 이용하여 리그오브레전드 게임의 최종승패를 랭크별로 예측하고, 구축된 승패예측 모형으로부터 변수중요도를 추출하여 승리를 위한 초반 게임운영의 방향성을 알아보았다. 그 결과 모든 랭크에서 70% 이상의 정확도로 승패를 예측할 수 있었다. 이는 경기 양상이 대부분 뒤집히지 않고 최종승패로 이어지는 것을 의미하며, 이러한 경향성은 상위 랭크로 갈수록 더욱 강하게 나타났다. 랭크와 무관하게 킬(데스)가 초반 게임에서 최종승패에 가장 큰 영향을 미치는 요소로 나타났으나, 일부 변수는 랭크에 따라 중요도 순위가 변화하였고 이는 유저가 속한 랭크에 따라 승리에 효과적인 초반 전략방향에 차이가 있음을 시사한다.

Keywords

Acknowledgement

This research was supported by the Ministry of Food and Drug Safety of the Republic of Korea (21163수입안516).

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