职业英雄联盟地图赛果的赛前配对比较建模
Pre-game paired-comparison modeling of professional League of Legends map outcomes
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中文总结 AI 辅助
本文构建单阶段逻辑回归与两阶段混合模型预测职业英雄联盟地图赛前胜率，两者统计等效，均优于经典基准，与市场预测接近。
中文摘要 AI 辅助
我们构建并评估了一个针对职业《英雄联盟》（LoL）中单张地图（“对局”）的赛前获胜概率预测器。所提出的模型是一个单阶段逻辑回归，端到端地在胜负对数损失上拟合：每支队伍对过去同侧结果的指数加权移动平均、一个岭收缩的稳定实力（即逻辑混合模型的最大后验估计）以及一个首抢选边协变量，该模型在样本外自然校准（前推斜率0.995）。它用稳定的队伍实力增强了纯动态Bradley–Terry规格。第二个独立构建的两阶段复合混合模型，采用限制最大似然（REML）和最佳线性无偏预测（BLUP）收缩，并辅以Platt校准，作为作者能构建的最强对手。在2024至2026年间六个区域联赛和三场国际赛事的5,135场对局中，在配对逐场Diebold–Mariano推断下，两种架构在每个协议和窗口上统计上无法区分（全局留出0.2230对0.2257；前推0.2207对0.2215），因此根据简约性而非准确性更偏好简单模型；两者均明显优于经典动态基准（0.2351），并更适度地优于静态拟合（0.2301/0.2268）。在928场匹配地图中，与Polymarket相比，其预测在自身逐场合约上与市场统计上无法区分，而市场的适度优势集中在跨赛区世界赛和系列赛决胜局地图上。
英文摘要
We build and evaluate a pre-game win-probability forecaster for individual maps (``games'') in professional \emph{League of Legends} (LoL). The proposed model is a one-stage logistic regression fit end-to-end on the win/loss log-loss: each team's exponentially-weighted moving average of past same-side results, a ridge-shrunk stable strength that is the maximum-a-posteriori estimate of a logistic mixed model, and a first-pick draft covariate, natively calibrated out of sample (walk-forward slope $0.995$). It augments a purely dynamic Bradley--Terry specification with stable team strengths. A second, independently built two-stage composite mixed model under restricted maximum likelihood (REML) and best linear unbiased prediction (BLUP) shrinkage, with Platt calibration, serves as the strongest rival the authors could build. On $5{,}135$ games across six regional leagues and three international events (2024--2026), under paired per-game Diebold--Mariano inference, the two architectures are statistically indistinguishable on every protocol and window (global holdout $0.2230$ vs.\ $0.2257$; walk-forward $0.2207$ vs.\ $0.2215$), so the simpler model is preferred on parsimony, not accuracy; both improve on the classical dynamic benchmark ($0.2351$) by a clear margin and on the static fits ($0.2301$/$0.2268$) more modestly. Against Polymarket on $928$ matched maps, the forecasts are statistically indistinguishable from the market on its own per-game contracts, with a modest market edge concentrated on cross-region Worlds and series-decider maps.
发表机构
- National Tsing Hua University（国立清华大学）
机构由 AI 辅助整理，请以论文原文为准。
