  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.
