Evaluating Team Skill Aggregation in Online Competitive Games

Dehpanah, Arman; Ghori, Muheeb Faizan; Gemmell, Jonathan; Mobasher, Bamshad

doi:10.1109/cog52621.2021.9618994

Cited by 3 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also implies each player contributes to the outcome of the game equally. The research of [5] focused on this assumption and turns out there are better options regarding rating a team. The three methods investigated to evaluate a team are MAX, MIN, and SUM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The three methods investigated to evaluate a team are MAX, MIN, and SUM. The SUM method is the one used by traditional rating systems, where a team's rating is the sum of all its members; the MAX method just takes the highest player ratings in the team and implies that a strong player can carry the team to victory; and the MIN method only considers the least skilled member in the team and implies the weak end can drag the entire team down [5]. Based on the results of three settings using traditional ratings (Elo, Glicko, and TrueSkill), it concludes that the MAX method outperforms the other two methods in terms of accuracy in predicting game outcomes [5].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Achieving fairness in team-based FPS games: A skill-based matchmaking solution

Wu,

Meng,

Chen

et al. 2024

ACE

View full text Add to dashboard Cite

Matchmaking is a critical part of online games which is often related to player satisfaction. To pursue a fair user experience, matchmaking mechanisms typically try to put players with similar skill levels into the same game. The traditional process relies heavily on the outcome of the game instead of the in-game performance of players. This paper proposes a new rating system to represent the skill level of both players and teams, along with a new definition to measure the degree of fairness of a matchmaking result. Three clustering methods (K-means, AGG and BKPP) are investigated to perform the matchmaking and the results are evaluated based on the newly-proposed definitions. The matchmaking results generated by the AGG method appear to reach the best degree of fairness. All source codes related to the project are available at https://github.com/WrtTZ/Achieving-Fairness-in-Team-Based-FPS-Games-A-Skill-Based-Matchmaking-Solution.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Achieving fairness in team-based FPS games: A skill-based matchmaking solution

Wu,

Meng,

Chen

et al. 2024

ACE

View full text Add to dashboard Cite

show abstract

“…Based on the results achieved in the previous section, here we assume the performance of a team is determined by the performance of its best player [19]. Therefore, we take the maximum rating of team members to create the team's rating.…”

Section: Rank Prediction Using Ratingsmentioning

confidence: 99%

Behavioral Player Rating in Competitive Online Shooter Games

Dehpanah¹,

Ghori²,

Gemmell³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played. However, the overall experience of players is shaped by factors beyond the sole outcome of their games. In this paper, we engineer several features from in-game statistics to model players and create ratings that accurately represent their behavior and true performance level. We then compare the estimating power of our behavioral ratings against ratings created with three mainstream rating systems by predicting rank of players in four popular game modes from the competitive shooter genre. Our results show that the behavioral ratings present more accurate performance estimations while maintaining the interpretability of the created representations. Considering different aspects of the playing behavior of players and using behavioral ratings for matchmaking can lead to matchups that are more aligned with players' goals and interests, consequently resulting in a more enjoyable gaming experience.

show abstract

“…However, one approach to accomplishing DDA in CSGs is player-level matchmaking. This method is based on welldocumented rating systems such as Elo (Elo 1978) and Glicko/Glicko-2 (Glickman 1999(Glickman , 2001, which have been used to measure the skill of players in games ranging from chess and Go (Au 2020) to Pokémon Showdown (Pokemon Showdown 2021) and Counter Strike: Global Offensive (Dehpanah et al 2021).…”

Section: Dynamic Difficulty Adjustmentmentioning

confidence: 99%

Effects of Player-Level Matchmaking Methods in a Live Citizen Science Game

Stoneman

Miller

Cooper

2022

AIIDE

View full text Add to dashboard Cite

Citizen science games must balance task difficulty with player skill to ensure optimal engagement and performance. This issue has been previously addressed via player-level matchmaking, a dynamic difficulty adjustment method in which player and level ratings are used to present levels best suited for players' individual abilities. However, this work has been done in small, isolated test games and left out potential techniques that could further improve player performance. Therefore, we examined the effects of player-level matchmaking in Foldit, a live citizen science game. An experiment with 221 players demonstrated that dynamic matchmaking approaches led to significantly more levels completed, as well as a more challenging highest level completed, compared to random level ordering, but not greater than a static approach. We conclude that player-level matchmaking is worth consideration in the context of live citizen science games, potentially paired with other dynamic difficulty adjustment methods.

show abstract

Evaluating Team Skill Aggregation in Online Competitive Games

Cited by 3 publications

References 14 publications

Achieving fairness in team-based FPS games: A skill-based matchmaking solution

Achieving fairness in team-based FPS games: A skill-based matchmaking solution

Behavioral Player Rating in Competitive Online Shooter Games

Effects of Player-Level Matchmaking Methods in a Live Citizen Science Game

Contact Info

Product

Resources

About