Champion recommendation in multiplayer online battle arena games is usually framed informally as a problem of metagame strength, personal comfort, or global win rate. We formalize champion recommendation in League of Legends as an interpretable, player-conditional ranking problem under sparse, noisy, and non-stationary behavioral data. The proposed framework combines four information sources: a population-strength proxy, player-style similarity, direct and indirect mastery priors, and archetype-level guardrails. The method uses robust median/MAD normalization, logarithmic transforms for skewed event counts, recency-weighted player style vectors, mastery-weighted champion-pool vectors, weighted cosine similarity, rank-scaled score components, and k-means++ clustering for coarse archetype support. The implemented prototype uses a Python/Pandas modeling layer, Supabase-backed storage, and a web-facing recommendation interface. Unlike black-box supervised win-prediction systems, the proposed method returns decomposed recommendation scores that can be inspected as expected-performance proxy, fit, mastery, and archetype compatibility. A single-player case study on a 100-game history for the player identifier DIVINERAINRACCON is included as an end-to-end sanity check. The manuscript is therefore a methods and systems contribution: it specifies a reproducible, modular, and auditable champion recommender and gives a validation protocol for future large-scale evaluation through temporal train-test splits, next-champion recovery, calibration analysis, and ablation studies.

翻译：多人在线战术竞技游戏中的英雄推荐通常被非正式地视为元游戏强度、个人舒适度或全局胜率问题。本文将《英雄联盟》中的英雄推荐形式化为一个在稀疏、含噪且非平稳行为数据下的可解释、玩家条件排序问题。所提出的框架融合了四种信息源：群体强度代理、玩家风格相似性、直接与间接精通先验，以及原型级约束。该方法采用稳健的中位数/MAD归一化、针对偏态事件计数的对数变换、带近因权重的玩家风格向量、带精通权重的英雄池向量、加权余弦相似度、基于排名的分数分量，以及用于粗粒度原型支持的k-means++聚类。实现的原型采用Python/Pandas建模层、Supabase支持的存储，以及面向网页的推荐界面。与黑盒监督式胜率预测系统不同，该方法返回可分解的推荐分数，这些分数可作为期望表现代理、拟合度、精通度和原型兼容性进行检查。以玩家标识符DIVINERAINRACCON的100场游戏历史为例进行单玩家案例研究，作为端到端的合理性验证。因此，本文贡献在于方法及系统层面：它详细说明了一个可复现、模块化且可审计的英雄推荐器，并给出了一个通过时间训练-测试分割、下一英雄恢复、校准分析和消融研究进行未来大规模评估的验证协议。