League of Legends: An Application of Classification Algorithms to Verify the Prediction Importance of Main In-Game Variables

• Alexandre C. S. Cruz Universidade Federal da Paraíba (UFPB)
• Thaís G. do Rêgo Universidade Federal da Paraíba (UFPB)
• Telmo de M. Filho Universidade Federal da Paraíba (UFPB)
• Yuri Malheiros Universidade Federal da Paraíba (UFPB)

DOI: https://doi.org/10.5753/sbgames_estendido.2021.19662

Resumo

League of Legends is currently one of the most popular video games with a competitive scene involving regional and international tournaments. In this paper, we predict the result of competitive matches using information about games played between the years 2016 and 2020 in the most well-known regional and international leagues, such as the Brazilian League of Legends Championship and League of Legends Pro League. We used several different approaches to train our models based on different variable categories. First, we used economic variables registered in the first 10 and 15 minutes of each match. Then we considered variables that change only from the beginning to the end of the game and do not suffer interference before and after the game. The accuracy of classifiers such as KNearest Neighbors, Random Forest, and Decision Tree varied from 68.33% to 85.17%, depending on which variables were used to train the models.

Palavras-chave: League of Legends, Machine Learning, Match Result Prediction

Referências

J. Lynch. ”As NFL ratings drop, a new internet study says young men like watching eSports more than traditional sports.” Business Insider, Entertainment, 2017. [link]. (accessed May 25, 2021).

AJ. Cortese, W. Chen. ”The US and China lead global gaming, but how are they developing the next generation of talent?” Kr Asia, Insights, 2020. [link]. (Accessed May 25, 2021)

Z. Yang and Y. Wang and P. Li and S. Lin and S. Shi and Shao-Lun Huang “Predicting Events in MOBA Games: Dataset, Attribution, and Evaluation,” 2020.

R. T. Souza. ”Aplicação de algoritmos classificadores para previsão de vitória em uma partida de League of Legends,” Advisor: M. N. Cortimiglia, 2017, p. 21, Graduation conclusion work - Production engineering, Federal University of Rio Grande do Sul, Porto Alegre, 2017. https://lume.ufrgs.br/handle/10183/179708 (Accessed May 25, 2021)

N. Kinkade, L. Jolla, and K. Lim, “Dota 2 win prediction,” Technical report, University of California, San Diego, Tech. Rep., 2015.

C. Kim and S. Lee, “Predicting Win-Loss of League of Legends Using Bidirectional LSTM Embedding,” KIPS Trans. Softw. and Data Eng, vol 9, pp. 61–68, Feb 2020. AJ. Cortese, W. Chen. ”The US and China lead global gaming, but how are they developing the next generation of talent?” Kr Asia, Insights, 2020. [link]. (Accessed May 25, 2021).

S. Berkovich. ”Understanding League of Legends Data Analytics” Medium, Snipe.gg, 2019. [link]. (Accessed May 5, 2021).

F. Pedregosa et al, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol 12, pp. 2825–2830, 2011.

K. Wimer. ”Betting is esports’ biggest and most underappreciated opportunity” Venture Beat, Games Beat, 2019. [link]. (Accessed June 8, 2021).