Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Quantitative Analysis for Win/Loss Prediction of 'League of Legends' Utilizing the Deep Neural Network System through Big Data

  • No, Si-Jae (Dept. of Comp. Engr. & African Languages, Hankuk University of Foreign Studies) ;
  • Moon, Yoo-Jin (Dept. of Mgmt. Information System, Hankuk University of Foreign Studies) ;
  • Hwang, Young-Ho (Division of Public Admin. & Economics, Kunsan National University)
  • Received : 2021.03.03
  • Accepted : 2021.04.22
  • Published : 2021.04.30
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Abstract

In this paper, we suggest the Deep Neural Network Model System for predicting results of the match of 'League of Legends (LOL).' The model utilized approximately 26,000 matches of the LOL game and Keras of Tensorflow. It performed an accuracy of 93.75% without overfitting disadvantage in predicting the '2020 League of Legends Worlds Championship' utilizing the real data in the middle of the game. It employed functions of Sigmoid, Relu and Logcosh, for better performance. The experiments found that the four variables largely affected the accuracy of predicting the match --- 'Dragon Gap', 'Level Gap', 'Blue Rift Heralds', and 'Tower Kills Gap,' and ordinary users can also use the model to help develop game strategies by focusing on four elements. Furthermore, the model can be applied to predicting the match of E-sports professional leagues around the world and to the useful training indicators for professional teams, contributing to vitalization of E-sports.

이 논문은 League of Legends (LOL) 게임의 승패를 예측하기 위하여 Deep Neural Network Model 시스템을 제안한다. 이 모델은 다양한 LOL 빅데이터를 활용하여 TensorFlow 의 Keras에 의하여 설계하였다. 연구 방법으로 한국 서버의 챌린저 리그에서 행해진 약 26000 경기 데이터 셋을 분석하여, 경기 도중 데이터를 수집하여 그 중에서 드래곤 처치 수, 챔피언 레벨, 정령, 타워 처치 수가 게임 결과에 유의미한 영향을 끼치는 것을 확인하였다. 이 모델은 Sigmoid, ReLu 와 Logcosh 함수를 사용했을 때 더 높은 정확도를 얻을 수 있었다. 실제 LOL의 프로 게임 16경기를 예측한 결과 93.75%의 정확도를 도출했다. 게임 평균시간이 34분인 것을 고려하였을 때, 게임 중반 15분 정도에 게임의 승패를 예측할 수 있음이 증명되었다. 본 논문에서 설계한 이 프로그램은 전 세계 E-sports 프로리그의 활성화, 승패예측과 프로팀의 유용한 훈련지표로 활용 가능하다고 사료된다.

Keywords

References

  1. Yong Chen, Hong Chen, Anjee Gorkhali, Yang Lu, Liqian Ma, and Ling Li, "Big Data Analytics and Big Data Science: A Survey," Journal of Management Analytics, Vol. 3, No. 1, pp. 1-42, 2016. https://doi.org/10.1080/23270012.2016.1141332
  2. Teradata, "Big Data Analytics - Reveal the Best Opportunities for Big Data Companies," 2021. https://kr.teradata.com/Solutions/Big-Data?utm_campaign=2020brand&utm_source=google&utm_medium=paidsearch&utm_content=TERA1_GS_Brand_APAC-KR-EN_CV_NBKW_BMM&utm_creative=BigData-DataAtTheCenter|BigDataOverview&utm_term=big%20data%20science&gclid=EAIaIQobChMIwLiR4o6C7wIV2KqWCh0TEw13EAAYASAAEgLGOvD_BwE
  3. Sangho Kim, "A Study on Relationship of BDBA (Big Data Business Analytics) System and Supply Chain Management," Journal of Korea Research Association of International Commerce, Vol. 19, No. 2, pp. 89-107, 2019. https://doi.org/10.29331/jkraic.2019.4.19.2.89
  4. Hyejeong Park, Kyoungha Seok, Juyong Shim and Changha Hwang, "Deep Learning from TensorFlow," Hanbit Academy Press, 2019.
  5. Bruce Lehrman, "Big Data's Role in the Post-COVID Era," Data Agility, Vol. 16, Issue 11, Sept. 2020. https://www.pipelinepub.com.
  6. Katy Warr, "Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery," O'Reilly Media, 2019.
  7. Wesley Chai, Mark Labbe, and Craig Stedman, "Big Data Analytics," 2021. https://searchbusinessanalytics.techtarget.com/definition/big-data-analytics
  8. Jun Wu, Jian Wang, Stephen Nicholas, Elizabeth Maitland, and Qiuyan Fan, "Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations," Journal of Medical Internet Research, Vol. 22, No. 10: e21980, Oct. 2020. DOI: 10.2196/21980.
  9. Jojo Moolayil, "Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python," Apress, 2019.
  10. Jen-Tzung Chien, "Source Separation and Machine Learning," Elsevier: Academic Press, 2019.
  11. Hon-Ki kim, Yu-Seop Kim. "League of Legends Win/Loss Prediction Using TensorFlow," Hallym University, 2017.
  12. Senpai.gg. https://senpai.gg
  13. Honqmei Li, Jinying Huang, and Shuwei Ji, "Bearing Fault Diagnosis with a Feature Fusion Method Based on An Ensemble Convolutional Neural Network and Deep Neural Network," Sensors (Basel, Switzerland), Vol. 19, Issue 9, pp. 2034, 2019. https://doi.org/10.3390/s19092034
  14. N. Yuvaraj, R. Arshath Raja, N.V. Kousik, Prashant Johri, and Mario Jose Divan, "Chapter15 - Analysis on the Prediction of Central Line-Associated Bloodstream Infections (CLABSI) Using Deep Neural Network Classification," Computational Intelligence and Its Applications in Healthcare, Academic Press, pp. 229-244, 2020. https://doi.org/10.1016/B978-0-12-820604-1.00016-9
  15. Gilbert Lim, Wynne Hsu, Mong Li Lee, Daniel Shu Wei Ting, and Tien Yin Wong, "Chapter 21 - Technical and Clinical Challenges of A.I. in Retinal Image Analysis," Computational Retinal Image Analysis::Tools, Applications and Perspectives, Academic Press, pp. 445-466, 2019. https://doi.org/10.1016/B978-0-08-102816-2.00022-8
  16. Xiangrui Xu, Yaqin Li, and Cao Yuan, "Identity Bracelets for Deep Neural Networks," IEEE Access, Vol. 8, pp. 102065-102074, 2020. DOI: 10.1109/ACCESS.2020.2998784
  17. Wonil Lee, Byungjai Kim, and HyunWook Park, "Quantification of Intravoxel Incoherent Motion with Optimized B-values Using Deep Neural Network," Magnetic Resonance in Medicine, Feb. 2021. DOI: 10.1002/mrm.28708
  18. 2020 Challenger Rank, https://developer.riotgames
  19. Junmo Cho, "Big Data Analytics and Artificial Intelligence Starting with Python," Infinity Books, 2020.
  20. Michael Negenevitsky, "Artificial Intelligence," Addison-Wesley, 2011.
  21. Russell, Stuart, "Artificial Intelligence: A Modern Approach," Pearson Education, 2017.
  22. Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep Learning," MIT Express, 2016.
  23. Ingook Cheon, "Artificial Intelligence: Machine Learning and Deep Learning by Python," Infinity Books, 2020.