Abstract
This work is aimed at finding behavioural patterns among professional players of League of Legends, one of the greatest recent phenomena in the world of video games. For that purpose, Hidden Markov Models (HMM) are used to model the sequence of events produced by a gameplay. First, the set of interesting game events for analysis is defined, and based on that, each gameplay of the dataset is transformed into a sequences of events. Then, four HMMs will be trained with the data from four different groups of sequences, according to the team that produces the events of the sequence (red/blue) and to whether that sequence led to a victory of the team or not. Finally, the resulting HMMs will be visualized and compared in order to achieve some conclusions about the macro game strategy in League of Legends, which will help to understand the game at the highest level of its competition.
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Notes
- 1.
League of Legends official website: https://euw.leagueoflegends.com/.
- 2.
R project for statistical computing: https://www.r-project.org/.
- 3.
LoLBehaviouralPatternsPublic: https://github.com/AlbertoMateosR/LoLBehaviouralPatternsPublic.
- 4.
Link to the dataset: https://www.kaggle.com/chuckephron/leagueoflegends.
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Ackowledgements
This work has been supported by several research grants: Spanish Ministry of Science and Education under TIN2014-56494-C4-4-P grant (DeepBio) and Comunidad Autónoma de Madrid under P2018/TCS-4566 grant (CYNAMON).
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Rama, A.M., Rodriguez-Fernandez, V., Camacho, D. (2020). Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_27
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