用聚类分析《守望先锋》角色搭配，发现隐藏的组合模式和平衡问题。
Beyond Win Rates: A Clustering-Based Approach to Character Balance Analysis in Team-Based Games
- 基于熵距离聚类，挖掘职业赛中角色共现规律。
- 识别出多组具有相似协同特征的角色组合。
- 适合游戏策划用于精细化角色平衡调整。
竞技游戏中角色多样性虽丰富了玩法，却常带来平衡难题，影响玩家体验与策略深度。传统评估依赖胜率、禁选率等综合指标，难以揭示团队对战中复杂的互动关系与角色分工。本文提出一种基于聚类的方法，利用Valorant Champions Tour 2022的职业比赛数据，通过层次聚类与Jensen-Shannon散度分析角色在队伍中的共现模式，识别出具有相似搭配特性的角色集群。该方法不仅补充了现有量化指标，还提供了更全面、可解释的角色协同与潜在失衡分析视角，为开发者提供情境感知的平衡优化工具。
原文摘要 · Abstract (English)
Character diversity in competitive games, while enriching gameplay, often introduces balance challenges that can negatively impact player experience and strategic depth. Traditional balance assessments rely on aggregate metrics like win rates and pick rates, which offer limited insight into the intricate dynamics of team-based games and nuanced character roles. This paper proposes a novel clustering-based methodology to analyze character balance, leveraging in-game data from Valorant to account for team composition influences and reveal latent character roles. By applying hierarchical agglomerative clustering with Jensen-Shannon Divergence to professional match data from the Valorant Champions Tour 2022, our approach identifies distinct clusters of agents exhibiting similar co-occurrence patterns within team compositions. This method not only complements existing quantitative metrics but also provides a more holistic and interpretable perspective on character synergies and potential imbalances, offering game developers a valuable tool for informed and context-aware balance adjustments.
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