Robust Player-Conditional Champion Ranking for League of Legends: Style Similarity, Mastery Priors, and Archetype-Constrained Discovery
Min Heo, Pranav Kadiyam, Prasun Panthi

TL;DR
This paper introduces an interpretable, player-conditional champion ranking framework for League of Legends that integrates multiple data sources and provides transparent recommendations, validated through a detailed case study and validation protocols.
Contribution
It presents a novel, modular champion recommendation method combining diverse information sources with interpretability and validation protocols, addressing challenges of sparse and noisy data.
Findings
The method produces decomposed scores for interpretability.
A case study demonstrates the system's practical application.
Validation protocols enable future large-scale evaluation.
Abstract
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…
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