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 documen
Record details
Published: 5 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Transparency
Retrieved: 6 August 2026
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How to cite this record
ethics.ai (5 August 2026), “From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking,” evidence record 16923, https://ethics.ai/record/16923 (originally published by arXiv cs.AI).
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