Abstract
In this paper we suggest the first systematic review and compare performance of most frequently used machine learning algorithms for prediction of the match winner from the teams’ drafts in Dota 2 computer game. Although previous research attempted this task with simple models, weve made several improvements in our approach aiming to take into account interactions among heroes in the draft. For that purpose we’ve tested the following machine learning algorithms: Naive Bayes classifier, Logistic Regression and Gradient Boosted Decision Trees. We also introduced Factorization Machines for that task and got our best results from them. Besides that, we found that model’s prediction accuracy depends on skill level of the players. We’ve prepared publicly available dataset which takes into account shortcomings of data used in previous research and can be used further for algorithms development, testing and benchmarking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Taylor, T.: Raising the Stakes: E-sports and the Professionalization of Computer Gaming. MIT Press, Cambridge (2012)
Ontanón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., Preuss, M.: A survey of real-time strategy game AI research and competition in starcraft. IEEE Trans. Comput. Intell. AI Games 5(4), 293–311 (2013)
Synnaeve, G., Bessiere, P.: A Bayesian model for opening prediction in RTS games with application to StarCraft. In: IEEE Conference on Computational Intelligence and Games (CIG), pp. 281–288. IEEE (2011)
Bosc, G., Tan, P., Boulicaut, J.-F., Raissi, C., Kaytoue, M.: A pattern mining approach to study strategy balance in RTS games. IEEE Trans. Comput. Intell. AI Games PP(99), 1–1 (2015)
Cavadenti, O., Codocedo, V., Boulicaut, J.-F., Kaytoue, M.: When cyberathletes conceal their game: clustering confusion matrices to identify avatar aliases. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE, October 2015
Conley, K., Perry, D.: How does he saw me? A recommendation engine for picking heroes in Dota 2. Technical report (2013)
Nuangjumnonga, T., Mitomo, H.: Leadership development through online gaming. In: 19th ITS Biennial Conference: Moving Forward with Future Technologies: Opening a Platform for All, Bangkok, pp. 1–24 (2012)
Pobiedina, N., Neidhardt, J.: On successful team formation. Technical report (2013)
Rioult, F., Métivier, J.-P., Helleu, B., Scelles, N., Durand, C.: Mining tracks of competitive video games. AASRI Procedia 8, 82–87 (2014). SECS
Drachen, A., Yancey, M., Maguire, J., Chu, D., Wang, I. Y., Mahlmann, T., Schubert, M., Klabajan, D.: Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (Dota 2). In: Games Media Entertainment (GEM), Dota 2, vol. 2, pp. 1–8. IEEE (2014)
Eggert, C., Herrlich, M., Smeddinck, J., Malaka, R.: classification of player roles in the team-based multi-player game Dota 2. In: Chorianopoulos, K., Divitini, M., Hauge, J.B., Jaccheri, L., Malaka, R. (eds.) ICEC 2015. LNCS, vol. 9353, pp. 112–125. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24589-8_9
Agarwala, A., Pearce, M.: Learning Dota 2 team compositions. Technical report, Stanford University (2014)
Song, K., Zhang, T., Ma, C.: Predicting the winning side of DotA2. Technical report, Stanford University (2015)
Kalyanaraman, K.: To win or not to win? A prediction model to determine the outcome of a DotA2 match. Technical report, University of California San Diego (2014)
Kinkade, N., Jolla, L., Lim, K.: Dota 2 win prediction. Technical report, University of California, San Diego (2015)
Johansson, F., Wikström, J., Johansson, F.: Result prediction by mining replays in Dota 2. Ph.D. thesis, Blekinge Institute of Technology (2015)
Rendle, S.: Factorization machines. In: Proceedings of the IEEE International Conference on Data Mining, ICDM 2010, Washington, DC, USA, pp. 995–1000. IEEE Computer Society (2010)
Freudenthaler, C., Schmidt-Thieme, L., Rendle, S.: Bayesian factorization machines. In: Workshop on Sparse Representation and Low-rank Approximation, Neural Information Processing Systems (NIPS-WS) (2011)
Bayer, I.: fastFM: a library for factorization machines. CoRR, abs/1505.00641 (2015)
Chen, T., Guestrin, C.: XGBoost: a Scalable Tree Boosting System. arXiv e-prints, March 2016
Acknowledgements
This paper was prepared within the framework of a subsidy granted to HSE by the Government of Russian Federation for implementation of the Global Competitiveness Program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Semenov, A., Romov, P., Korolev, S., Yashkov, D., Neklyudov, K. (2017). Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-52920-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52919-6
Online ISBN: 978-3-319-52920-2
eBook Packages: Computer ScienceComputer Science (R0)