Browse > Article
http://dx.doi.org/10.7583/JKGS.2021.21.4.3

Predicting Game Results using Machine Learning and Deriving Strategic Direction from Variable Importance  

Kim, Yongwoo (Graduate School of Technology & Innovation Management, Hanyang University)
Kim, Young‐Min (Graduate School of Technology & Innovation Management, Hanyang University)
Publication Information
Journal of Korea Game Society / v.21, no.4, 2021 , pp. 3-12 More about this Journal
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.
Keywords
Machine Learning; Classification; League of Legends;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bzdok, D., Altman, N., and Krzywinski, M., "Statistics versus machine learning", Nature methods, 15(4), pp 233-234, 2018.   DOI
2 Rich Caruana and Alexandru Niculescu-Mizil, "An empirical comparison of supervised learning algorithms", In Proceedings of the 23rd international conference on Machine learning, pp.161-168, 2006.
3 Van Saeys, Inaki Inza and Pedro Larranaga, "A review of feature selection techniques in bioinformatics", Bioinformatics, Volume 23, Issue 19, pp. 2507-2517, 2007.   DOI
4 Nicodemus K.K., "Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures", Brief Bioinform, vol. 12, No. 4, pp.369-373, 2011.   DOI
5 Silva, A., G. Pappa and L. Chaimowicz, "Continuous Outcome Prediction of League of Legends Competitive Matches Using Recurrent Neural Networks.", In Proceedings of SBGames, code: 188226, 2018.
6 Seong-Eun Seo and Chi-Yo Kim, "Recognition of the Type and Cause of Trolling", Korea Game Society, vol. 15, No. 4, pp. 93-110, 2015.   DOI
7 Bentejac C., Csorgo, A. and Martinez-Munoz G., "A comparative analysis of gradient boosting algorithms", Artificial Intelligence Review, vol. 54, pp.1937-1967, 2021.   DOI
8 Kyu Bok Lee and Young Jae Kim, "Analysis of Factors that Influence Users' Preference for MOBA Game Genre: Focusing on the Game Systems of League of Legends", Global Cultural Contents, vol. 47, pp. 107-124, 2021.
9 Ellis Cashmore. "Making Sense of Sports", Routledge, 2010.
10 Jimin Ku and Jaehee Kim, "Development of game indicators and winning forecasting models with game data", Journal of the Korean Data and Information Science Society, vol. 28, no. 2, pp.237-250, 2017.   DOI
11 OP.GG, "LoL Stats, Record Replay, Database, Guide - OP.GG", accessed Apr 13, 2021, https://www.op.gg.
12 일요서울, "사교육 시장에 등장한 '게임학원', 그 배경은?", accessed Apr 13, 2021, http://www.ilyoseoul.co.kr/news/articleView.html?idxno=237470.
13 Korea Creative Content Agency, "The 2020 Survey on the Korean e-Sports Industry", Korea Creative Content Agency, 2020.
14 Jung Hwan Cho, "Utilization and Prospect of Sport Big Data", Korean Society For Measurement And Evaluation In Physical Education And Sports Science, vol. 14, No. 3, pp.01-11, 2012.
15 Yong Goo Kang and Huy Kang Kim, "Game Bot Detection Based on Action Time Interval", Journal of The Korea Institute of Information Security and Cryptology, vol. 28, No. 5, pp.1153-1160, 2018.   DOI
16 GGtics, "Analytics/AI to help players win - YOUR.GG", accessed Apr 13, 2021, https://your.gg/?lang=en.
17 Alexander Neumann, "Developing a Model to Predict Match Outcomes in League of Legends," Barrett, The Honors College Thesis, Arizona State University, 2015.
18 Cheolgi Kim and Soowon Lee, "Predicting Win-Loss of League of Legends Using Bidirectional LSTM Embedding", Korea Information Processing Society, vol. 9, No. 2, pp.61-68, 2020.
19 Dong-Wook Kim, Jeawon Park and Jaehyun Choi, "A study for the prediction of winning of e-Sports using machine Learning", Jounal of The Korea Society of Information Technology Policy & Management, vol. 9, No. 1, pp.319-325, 2017.
20 Sae-Sook Oh and Dae-Hoon Kim, "Analysis of the Academic Research Trend of e-sports", Journal of Wellness, vol. 7, No. 2, pp.113-121, 2012.
21 Gregorutti B., Michel B. and Saint-Pierre P, "Correlation and variable importance in random forests", Statistics and Computing, vol. 27, pp.659-678, 2017.   DOI
22 Min-ji Oh, Eun-seon Choi, Som Akhamixay Oui and Wan-sup Cho, "Predicting win-loss using game data and deriving the importance of subdivided variables", The Korea Journal of BigData, vol. 5, No. 2, pp.231-240, 2020.   DOI
23 Ani, R., Harikumar, V., Devan, A. K., and Deepa, O. S., "Victory prediction in League of Legends using Feature Selection and Ensemble methods", In 2019 International Conference on Intelligent Computing and Control Systems, pp. 74-77, 2018.
24 Thompson JJ, Blair MR, Chen L and Henrey AJ, "Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning", PLoS ONE 8(9): e75129.   DOI
25 Lee, Sang-Kwang, Seung-Jin Hong, and Seong-Il Yang. "Predicting Game Outcome in Multiplayer Online Battle Arena Games", International Conference on Information and Communication Technology Convergence, IEEE, 2019.
26 Riot Games, "Riot Developer Portal", accesse d Apr 13, 2021, https://developer.riotgames.com.