用LLM预测足球比分？论文把Dixon-Coles和LLM结合成可审计管道，英超前150场Top-3精确率从26.7%提到30.7%，还说了哪里没用。
该论文提出一个可审计的LLM信息管道，将动态Dixon-Coles统计模型与LLM情境推理结合，用于足球精确比分预测。论文记录了V1到V4四次迭代：V1为Dixon-Coles基线，V2将LLM评级映射到预期进球参数，V3用逐球模拟替代标量修正，V4加入首破门与后续级联判断。在2025-26英超前150场比赛的时间回放中，V1的Top-1精确比分准确率为10.0%，Top-3为26.7%；V4提升至14.7%和30.7%，候选覆盖率从77.3%增至84.7%。V1的原生1X2分布的argmax准确率为53.3%，对数损失0.9878，Brier分数0.5870。结果属于探索性，开发切片并非未触碰的基准，且时间输入隔离无法排除封闭LLM中的结果记忆。
From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking
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-goal cascade judgments, time-aware stopping, and deterministic tail candidates. The harness defines input semantics, supplies pre-match evidence, and constrains the LLM to an inspectable reasoning route. On a chronological replay of the first 150 matches of the 2025-26 English Premier League, V1 achieved 10.0% Top-1 and 26.7% Top-3 exact-score accuracy. V3 reached 12.0% and 30.0%, while V4 reached 14.7% and 30.7%. V4 increased candidate coverage from 77.3% to 84.7%, although no added tail candidate became a Top-3 exact hit. V1's native 1X2 distribution achieved 53.3% argmax accuracy, 0.9878 log loss, 0.5870 Brier score, and 0.2095 ranked probability score. These results are exploratory: the development slice is not an untouched benchmark, and temporal input isolation cannot exclude outcome memory in a closed LLM. The contribution is an auditable hybrid architecture, a clear design evolution, and negative findings showing where football-aware simulation does and does not improve score selection.