DeepSkill: A methodology for measuring teams’ skills in massively multiplayer online games

Rezapour, Mohammad Mahdi; Fatemi, Afsaneh; Nematbakhsh, Mohammad Ali

doi:10.1007/s11042-023-15796-x

Multimed Tools Appl

2023

DOI: 10.1007/s11042-023-15796-x

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DeepSkill: A methodology for measuring teams’ skills in massively multiplayer online games

Mohammad Mahdi Rezapour,

Afsaneh Fatemi,

Mohammad Ali Nematbakhsh

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2024

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Cited by 1 publication

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Skill-Based Matchmaking for Competitive Two-Player Games

Yuksel

2024

Proc. ACM Comput. Graph. Interact. Tech.

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Skill-based matchmaking is a crucial component of competitive multiplayer games and it is directly tied to how the players would enjoy the game. We present a simple matchmaking algorithm that aims to achieve a target win rate for all players, making long win/loss streaks less probable. It is based on the estimated skill levels of players. Therefore, we also present a rating estimation for players that does not require any game-specific information and purely relies on game outcomes. Our evaluation shows that our methods are effective in estimating a player's rating, responding to changes in rating, and achieving a desirable win rate that avoids long win/loss streaks in competitive two-player games.

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Skill-Based Matchmaking for Competitive Two-Player Games

Yuksel

2024

Proc. ACM Comput. Graph. Interact. Tech.

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show abstract

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DeepSkill: A methodology for measuring teams’ skills in massively multiplayer online games

Cited by 1 publication

References 38 publications

Skill-Based Matchmaking for Competitive Two-Player Games

Skill-Based Matchmaking for Competitive Two-Player Games

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