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Conference Paper
An Analysis of the Effects of Physical Abilities on RTS Game Outcome Using Machine Learning Approach
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- Authors
- Dae-Wook Kim, Sungyun Park, Seong-il Yang
- Issue Date
- 2019-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.929-933
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Physical ability in games represents how many actions or multitasks a gamer can do or how fast the gamer can react. In this paper, we characterize physical ability features from Starcraft II data. The proposed win prediction model based on machine learning algorithms evaluates how much features affect a game outcome. Even if features are used only related to physical ability, prediction result shows higher accuracy on a small scale than one with score-related features. Finally, we analyze those features and conclude that although matchmaking rating(MMR) plays primary role to lead a game win, actions per minute and others are also crucial factor to win.
- KSP Keywords
- Machine Learning Algorithms, Machine Learning Approach, RTS games, Small-scale, StarCraft II, Win prediction, model-based, prediction model