QuickSkill: Novice Skill Estimation in Online Multiplayer Games

TL;DR

QuickSkill is a deep learning framework that rapidly estimates novice players' skills in online multiplayer games, improving matchmaking fairness during early gameplay stages by accurately predicting future skill levels.

Contribution

This paper introduces QuickSkill, the first deep learning-based approach to address the cold-start problem in skill estimation for online multiplayer matchmaking.

Findings

QuickSkill achieves accurate skill prediction with limited initial game data.

Using QuickSkill reduces team skill disparities in matchmaking.

Experiments show improved player experience and fairness.

Abstract

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.