Player Skill Decomposition in Multiplayer Online Battle Arenas
This work addresses team formation and player experience enhancement in multiplayer online games, but it is incremental as it applies existing predictive models to new data.
The paper tackled the problem of decomposing player skills in MOBA games to identify essential factors for match outcomes, finding that in League of Legends, base avatar skills, base player skills, and champion-specific skills are prominent, while in DOTA2, only base avatar skills significantly impact outcomes.
Successful analysis of player skills in video games has important impacts on the process of enhancing player experience without undermining their continuous skill development. Moreover, player skill analysis becomes more intriguing in team-based video games because such form of study can help discover useful factors in effective team formation. In this paper, we consider the problem of skill decomposition in MOBA (MultiPlayer Online Battle Arena) games, with the goal to understand what player skill factors are essential for the outcome of a game match. To understand the construct of MOBA player skills, we utilize various skill-based predictive models to decompose player skills into interpretative parts, the impact of which are assessed in statistical terms. We apply this analysis approach on two widely known MOBAs, namely League of Legends (LoL) and Defense of the Ancients 2 (DOTA2). The finding is that base skills of in-game avatars, base skills of players, and players' champion-specific skills are three prominent skill components influencing LoL's match outcomes, while those of DOTA2 are mainly impacted by in-game avatars' base skills but not much by the other two.