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.
|Title of host publication
|Proceedings of the Australasian Computer Science Week Multiconference 2021, ACSW 2021
|Nigel Stanger, Veronica Liesaputra Joachim
|Association for Computing Machinery, Inc
|Number of pages
|Published - 1 Feb 2021
|2021 Australasian Computer Science Week Multiconference, ACSW 2021 - Virtual, Online, New Zealand
Duration: 1 Feb 2021 → 5 Feb 2021
|ACM International Conference Proceeding Series
|2021 Australasian Computer Science Week Multiconference, ACSW 2021
|1/02/21 → 5/02/21
Bibliographical noteFunding Information:
Part of this research is supported by the Competence Center for Machine Learning Rhine Ruhr (ML2R) which is funded by the Federal Ministry of Education and Research of Germany (grant no. 01—S18038B). The authors would like to thank the anonymous reviews for their insightful comments.
© 2021 ACM.
- Anomaly Detection
- Archetypal Analysis
- Multiplayer Online Battle Arena