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Archetypal Analysis Based Anomaly Detection for Improved Storytelling in Multiplayer Online Battle Arena Games

 
: Sifa, R.; Drachen, A.; Block, F.; Moon, S.; Dubhashi, A.; Xiao, H.; Li, Z.; Klabjan, D.; Demediuk, S.

:

Stanger, N. ; Association for Computing Machinery -ACM-:
Australasian Computer Science Week Multiconference, ACSW 2021. Proceedings : 1st-5th February 2021, Dunedin, New Zealand (virtual)
New York: ACM, 2021
ISBN: 978-1-4503-8956-3
Art. 24, 8 S.
Australasian Computer Science Week Multiconference (ACSW) <2021, Online>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
Anomalies in esports refer to situations when something unexpected or unlikely happens. Rapid performance changes, unusual strategies, extraordinary plays, accelerated resource gains or team wipeouts comprise examples, but anomalies fundamentally comprise any situation where something unexpected happens. In multi-player online esports games such as multi-player online battle arena games, anomalies form a key component of the commentator-driven storytelling. In fast-paced, complex esports titles, anomalies can however go undetected until it is too late for commentators to note them and use them in their coverage, and for viewers they can take place outside the viewable area of the broadcast stream. Furthermore, there are limited tools available for commentators and players across professional and amateur levels for analysing or categorising anomalies. The research presented here provides a novel approach towards identifying one type of outliers in esports matches, via the application of archetype analysis to extract novel insights that can be used by commentators to improve esports coverage. As a case example, the major esports title League of Legends is used. We present a viable methodology for utilizing distributions resulting from the archetypal clusters and reconstruction errors to expose and explain anomalous events during gameplay.

: http://publica.fraunhofer.de/dokumente/N-637569.html