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A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League

Romain Mathonat 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 : Competitive gaming, or esports, is now well-established and brought the game industry in a novel era. It comes with many challenges among which evaluating the level of a player, given the strategies and skills she masters. We are interested in automatically identifying the so called skillshots from game traces of Rocket League, a "soccer with rocket-powered cars" game. From a pure data point of view, each skill execution is unique and standard pattern matching may be insufficient. We propose a non trivial data-centric approach based on pattern mining and supervised learning techniques. We show through an extensive set of experiments that most of Rocket League skillshots can be efficiently detected and used for player modelling. It unveils applications for match making, supporting game commentators and learning systems among others.
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Submitted on : Tuesday, August 25, 2020 - 12:25:41 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM
Long-term archiving on: : Tuesday, December 1, 2020 - 7:40:40 PM


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  • HAL Id : hal-02921566, version 1


Romain Mathonat, Jean-François Boulicaut, Mehdi Kaytoue. A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League. IEEE Conference on Games 2020, Aug 2020, Online, Japan. pp. 267-274. ⟨hal-02921566⟩



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