League of Legends (LoL) has been a dominant esport for a decade, yet the inherent complexity of the game has stymied the creation of analytical measures of player skill and performance. Current industry standards are limited to easy-to-procure individual player statistics that are incomplete and lacking context as they do not take into account teamplay or game state. We present a unified performance model for League of Legends which blends together measures of a player's contribution within the context of their team, insights from traditional sports metrics such as the Plus-Minus model, and the intricacies of LoL as a complex team invasion sport. Using hierarchical Bayesian models, we outline the use of gold and damage dealt as a measure of skill, detailing players' impact on their own-, their allies'- and their enemies' statistics throughout the course of the game. Our results showcase the model's increased efficacy in separating professional players when compared to a Plus-Minus model and to current esports industry standards, while metric quality is rigorously assessed for discrimination, independence, and stability. Readers might also find additional qualitative analytics which explore champion proficiency and the impact of collaborative team-play. Future work is proposed to refine and expand the SIDO performance model, offering a comprehensive framework for esports analytics in team performance management, scouting and research realms.

翻译：《英雄联盟》（LoL）作为主导电竞项目已长达十年，但游戏固有限制性复杂性阻碍了衡量玩家技能与表现的分析方法发展。当前行业标准局限于易获取的个体玩家统计指标，这些指标因未纳入团队协作或游戏状态而存在不完整性与缺乏上下文关联的缺陷。我们提出一种统一的《英雄联盟》表现模型，该模型融合了团队情境下的玩家贡献度量、传统体育指标（如正负值模型）的洞见，以及《英雄联盟》作为复杂团队入侵运动的特性。通过分层贝叶斯模型，我们以金币与伤害输出作为技能衡量指标，详细阐述了玩家对自身、队友及敌方统计指标在游戏过程中的影响。结果表明，相较于正负值模型及当前电竞行业标准，该模型在区分职业玩家方面展现出更高效能，同时通过区分度、独立性与稳定性等维度对指标质量进行了严格评估。读者还可发现关于英雄熟练度与团队协作影响的补充性质性分析。未来研究将致力于完善并扩展SIDO表现模型，为团队绩效管理、人才发掘及电竞研究领域提供综合分析框架。