What did I do Wrong in my MOBA Game?: Mining Patterns Discriminating Deviant Behaviours

Olivier Cavadenti 1 Victor Codocedo 1 Jean-François Boulicaut 1 Mehdi Kaytoue 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : The success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments , brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, " Defense of the Ancients 2 " , commonly known simply as DOTA2. In this context, a challenge is to propose data analysis methods and metrics that help players to improve their skills. We design a data mining-based method that discovers strategic patterns from historical behavioral traces: Given a model encoding an expected way of playing (the norm), we are interested in patterns deviating from the norm that may explain a game outcome from which player can learn more efficient ways of playing. The method is formally introduced and shown to be adaptable to different scenarios. Finally, we provide an experimental evaluation over a dataset of 10, 000 behavioral game traces.
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Olivier Cavadenti, Victor Codocedo, Jean-François Boulicaut, Mehdi Kaytoue. What did I do Wrong in my MOBA Game?: Mining Patterns Discriminating Deviant Behaviours. International Conference on Data Science and Advanced Analytics, Oct 2016, Montréal, Canada. ⟨hal-01388321⟩

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