1 free read left. Create a free account for unlimited reading + AI research.
Create free accountWhitepaper
DiGRA
The study examines how high-level competitors navigate a metagame that shifts constantly because of frequent game patches.
This study examines metagaming practices among high-level players of League of Legends, focusing on how competitors navigate a strategic metagame that shifts constantly because of frequent game patches. Drawing on theoretical definitions of the metagame from Garfield, Salen and Zimmerman, and Carter and colleagues, the research argues that metagaming in this context extends beyond mechanical skill to include the community-enforced strategic norms that govern how players approach their roles and positions upon entering a match.
The findings are based on interviews with thirteen high-level players from the Korean and Oceanic servers, conducted at the close of the 2015 competitive season. Participants consistently reported that game updates disrupted their established position and role combinations, and most responded either by switching to other pre-existing combinations or by maintaining their habitual style even when it reduced their enjoyment. Strikingly, none of these players considered moving beyond the strategic norms as a response to patches. Only one interviewee described deliberately subverting these norms, using alternative positions and role combinations to reach the second-highest ranked tier, and he reported never having received behavioural reprimands despite the disapproval such play often attracts from others.
The study concludes that the community's strong aversion to subversive play persists even where such approaches are demonstrably viable and permitted by both developer and system. This raises questions about the presumed effectiveness of strategic norms, the significance of subversion within competitive play, and the broader relationship between player communities and developers in online multiplayer games subject to frequent change. The research is presented as an extended abstract and is ongoing, so its conclusions reflect a limited qualitative sample rather than broad population-level data.
DiGRA · 2023