Player Skill Decomposition in Multiplayer Online Battle Arenas

TL;DR

This paper investigates the components of player skills in MOBA games like LoL and DOTA2, using predictive models to understand how different skill factors influence match outcomes, aiding game design and player development.

Contribution

It introduces a method for decomposing player skills into interpretable factors in MOBA games and applies it to analyze two popular titles, revealing key skill components affecting game results.

Findings

In LoL, avatar skills, player skills, and champion-specific skills are key.

In DOTA2, avatar base skills are influential, but other skills are less impactful.

The approach helps identify essential skill factors for game outcome prediction.

Abstract

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.

Full text

See pages 1-last of player-skill-decomposition.pdf