A Machine Learning Based Predictive Analysis Use Case for eSports Games

Year 2023, Volume: 3 Issue: 1, 25 - 35, 01.05.2023

Atakan Tuzcu , Emel Gizem Ay , Ayşegül Umay Uçar , Deniz Kılınç

Abstract

League of Legends (LoL) is a popular multiplayer online battle arena (MOBA) game that is highly recognized in the professional esports scene due to its competitive environment, strategic gameplay, and large prize pools. This study aims to predict the outcome of LoL matches and observe the impact of feature selection on model performance using machine learning classification algorithms on historical game data obtained through the official API provided by Riot Games. Detailed examinations were conducted at both team and player levels, and missing data in the dataset were addressed. A total of 1045 data were used for training team-based models, and 5232 data were used for training player-based models. Seven different machine learning models were trained and their performances were compared. Models trained on team data achieved the highest accuracy of over 98% with the AdaBoost algorithm. The top 10 features that had the most impact on the prediction outcome were identified among the 47 features in the dataset, and a new dataset was created from team data to retrain the models. After feature selection, the results showed that the accuracy of Logistic Regression increased from 89% to 98% and the accuracy of Gradient Boosting algorithm increased from 96% to 98%.

Keywords

league of legends, riot game, machine learning, random forest, gradient boosting

References

• [1] Mora-Cantallops, M., & Sicilia, M. Á. (2018). MOBA games: A literature review. Entertainment computing, 26, 128-138.
• [2] Yang, Y., Qin, T., & Lei, Y. H. (2016). Real-time e-sports match result prediction. arXiv preprint arXiv:1701.03162.
• [3] Silva, A. L. C., Pappa, G. L., & Chaimowicz, L. (2018). Continuous outcome prediction of league of legends competitive matches using recurrent neural networks. In SBC-Proceedings of SBCGames (pp. 2179-2259).
• [4] Medsker, L. R., & Jain, L. C. (2001). Recurrent neural networks. Design and Applications, 5, 64-67.
• [5] Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586.
• [6] Dey, R., & Salem, F. M. (2017, August). Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS) (pp. 1597-1600). IEEE.
• [7] Hitar-Garcia, J. A., Moran-Fernandez, L., & Bolon-Canedo, V. (2022). Machine learning methods for predicting league of legends game outcome.
• [8] Shen, Q. (2022, February). A machine learning approach to predict the result of League of Legends. In 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE) (pp. 38-45). IEEE.
• [9] Bahrololloomi, F., Sauer, S., Klonowski, F., Horst, R., & Dörner, R. (2022). A Machine Learning based Analysis of e-Sports Player Performances in League of Legends for Winning Prediction based on Player Roles and Performances. In VISIGRAPP (2: HUCAPP) (pp. 68-76).
• [10] Do, T. D., Wang, S. I., Yu, D. S., McMillian, M. G., & McMahan, R. P. (2021, August). Using machine learning to predict game outcomes based on player-champion experience in League of Legends. In Proceedings of the 16th International Conference on the Foundations of Digital Games (pp. 1-5).
• [11] Hodge, V. J., Devlin, S., Sephton, N., Block, F., Cowling, P. I., & Drachen, A. (2019). Win prediction in multiplayer esports: Live professional match prediction. IEEE Transactions on Games, 13(4), 368-379.
• [12] Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1), 1-13.
• [13] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• [14] Freund, Y., & Mason, L. (1999, June). The alternating decision tree learning algorithm. In icml (Vol. 99, pp. 124-133).
• [15] LaValley, M. P. (2008). Logistic regression. Circulation, 117(18), 2395-2399.
• [16] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
• [17] Murphy, K. P. (2006). Naive bayes classifiers. University of British Columbia, 18(60), 1-8.
• [18] Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
• [19] Schapire, R. E. (2013). Explaining adaboost. In Empirical inference (pp. 37-52). Springer, Berlin, Heidelberg.
• [20] Liang, J. (2022). Confusion Matrix: Machine Learning. POGIL Activity Clearinghouse, 3(4).
• [21] Fashoto, S. G., Mbunge, E., Ogunleye, G., & den Burg, J. V. (2021). Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination. Malaysian Journal of Computing (MJoC), 6(1), 679-697.