CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System

• URL: http://arxiv.org/abs/2406.19720v1
• Date: Fri, 28 Jun 2024 08:09:55 GMT
• Title: CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System
• Authors: Ge Fan, Chaoyun Zhang, Kai Wang, Yingjie Li, Junyang Chen, Zenglin Xu,
• Abstract summary: CUPID aims to optimize team and position assignments to improve both fairness and player satisfaction. It incorporates a pre-filtering step to ensure a minimum level of matchmaking quality, followed by a pre-match win-rate prediction model. Experiments were conducted on two large-scale, real-world MOBA datasets to validate the effectiveness of CUPID.
• Score: 38.36310386543932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multiplayer online battle arena (MOBA) genre has gained significant popularity and economic success, attracting considerable research interest within the Human-Computer Interaction community. Enhancing the gaming experience requires a deep understanding of player behavior, and a crucial aspect of MOBA games is matchmaking, which aims to assemble teams of comparable skill levels. However, existing matchmaking systems often neglect important factors such as players' position preferences and team assignment, resulting in imbalanced matches and reduced player satisfaction. To address these limitations, this paper proposes a novel framework called CUPID, which introduces a novel process called ``re-matchmaking'' to optimize team and position assignments to improve both fairness and player satisfaction. CUPID incorporates a pre-filtering step to ensure a minimum level of matchmaking quality, followed by a pre-match win-rate prediction model that evaluates the fairness of potential assignments. By simultaneously considering players' position satisfaction and game fairness, CUPID aims to provide an enhanced matchmaking experience. Extensive experiments were conducted on two large-scale, real-world MOBA datasets to validate the effectiveness of CUPID. The results surpass all existing state-of-the-art baselines, with an average relative improvement of 7.18% in terms of win prediction accuracy. Furthermore, CUPID has been successfully deployed in a popular online mobile MOBA game. The deployment resulted in significant improvements in match fairness and player satisfaction, as evidenced by critical Human-Computer Interaction (HCI) metrics covering usability, accessibility, and engagement, observed through A/B testing. To the best of our knowledge, CUPID is the first re-matchmaking system designed specifically for large-scale MOBA games.

Related papers

• QuickSkill: Novice Skill Estimation in Online Multiplayer Games [19.364132825629465]
  Current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. This is known as the ''cold-start'' problem for matchmaking rating algorithms. This paper proposes QuickSKill, a deep learning based novice skill estimation framework.
  arXiv  Detail & Related papers  (2022-08-15T11:59:05Z)
• Behavioral Player Rating in Competitive Online Shooter Games [3.203973145772361]
  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. 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 match-ups that are more aligned with players' goals and interests.
  arXiv  Detail & Related papers  (2022-07-01T16:23:01Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Evaluating Team Skill Aggregation in Online Competitive Games [4.168733556014873]
  We present an analysis of the impact of two new aggregation methods on the predictive performance of rating systems. Our evaluations show the superiority of the MAX method over the other two methods in the majority of the tested cases. Results of this study highlight the necessity of devising more elaborated methods for calculating a team's performance.
  arXiv  Detail & Related papers  (2021-06-21T20:17:36Z)
• The Evaluation of Rating Systems in Team-based Battle Royale Games [4.168733556014873]
  This paper explores the utility of several metrics for evaluating three popular rating systems on a real-world dataset of over 25,000 team battle royale matches. normalized discounted cumulative gain (NDCG) demonstrated more reliable performance and more flexibility.
  arXiv  Detail & Related papers  (2021-05-28T19:22:07Z)
• Generating Diverse and Competitive Play-Styles for Strategy Games [58.896302717975445]
  We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
  arXiv  Detail & Related papers  (2021-04-17T20:33:24Z)
• Multi-Agent Collaboration via Reward Attribution Decomposition [75.36911959491228]
  We propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge. CollaQ is evaluated on various StarCraft Attribution maps and shows that it outperforms existing state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-10-16T17:42:11Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)
• Competitive Balance in Team Sports Games [8.321949054700086]
  We show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model.
  arXiv  Detail & Related papers  (2020-06-24T14:19:07Z)
• Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks [48.5733173329785]
  We present Neural MMO, a massively multiagent game environment inspired by MMOs. We discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.
  arXiv  Detail & Related papers  (2020-01-31T18:50:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.