AI 中文总结
提出一种自适应Glicko-2评分框架，通过引入胜分差调整、优势加权、结构性冲击、主场优势建模和有序逻辑平局模型，实现动态球队实力估计和概率预测，并用于蒙特卡洛赛季模拟。
AI 中文摘要
足球比赛结果预测是一个具有挑战性的问题，因为球队实力随时间变化，比赛结果包含高度随机性，且平局在结果结构中起核心作用。经典的评分系统如Elo提供了简单且可解释的球队能力动态总结，但它们没有明确建模不确定性，并且常常忽略足球特定的上下文信息。本文提出了一种基于自适应Glicko-2的评分框架，用于概率足球预测和联赛级别的赛季模拟。该框架通过引入足球特定机制扩展了标准Glicko-2模型，包括胜分差调整、优势加权、结构性冲击、主场优势建模和有序逻辑平局模型。该框架动态估计潜在球队实力，将评分差异转化为胜-平-负概率，并利用这些概率通过蒙特卡洛抽样模拟联赛赛季的剩余部分。
英文摘要
Football match outcome prediction is a challenging problem because team strength changes over time, match outcomes contain a high level of randomness, and draws play a central role in the result structure. Classical rating systems such as Elo provide simple and interpretable dynamic summaries of team ability, but they do not explicitly model uncertainty and often ignore football-specific contextual information. This paper proposes an adaptive Glicko-2-based rating framework for probabilistic football forecasting and leaguelevel season simulation. The proposed framework extends the standard Glicko-2 model by incorporating football-specific mechanisms, including margin-of-victory adjustment, dominance weighting, structural shocks, home advantage modelling, and an ordered-logit draw model. The framework estimates latent team strength dynamically, converts rating differences into win-draw-loss probabilities, and uses these probabilities to simulate the remaining part of a league season through Monte Carlo sampling.
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