From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking· 从得分矩阵到足球感知的比赛状态模拟
Football score forecasting combines a strong statistical core with a difficult contextual edge. Dynamic Poisson-family models estimate team strength, expected goals, and coherent score probabilities, but do not directly understand roles, tactical matchups, motivation, or how a first goal changes behaviour. Large language models (LLMs) can reason about such concepts, yet are not calibrated probability engines. We combine both components through an auditable information harness. This paper documents four iterations: V1, a dynamic score-driven Dixon-Coles baseline; V2, which maps LLM contextual ratings back into expected-goal parameters; V3, which replaces scalar correction with goal-by-goal simulations over a frozen score-candidate set; and V4, which adds shared first-breakthrough and post-g
融合得分矩阵和语言模型，提升足球比分预测准确性。
- 核心方法
- 通过四个迭代版本（V1-V4），结合动态Poisson模型和大型语言模型，实现对比赛状态的模拟及精确比分的重排。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有的足球比分预测模型未能充分考虑比赛中的动态因素和背景信息。
- 关键实验
- 未提供
- 主要贡献
- 提出了一种可审计的信息框架，融合了统计模型和语言模型的优势，更好地预测足球比分及比赛变化。
- 意义与局限
- 该方法将提高足球比分预测的准确性，同时保持模型的透明度，便于审计和调整，但可能需要更多实验证据。