Football Match Insight Analysis Report

Problem

Football match analysis is often scattered across odds, form, injuries, weather, and model probabilities. Manual notes can drift, and value points or stake allocation may become hard to reproduce. This skill turns an input such as “home team vs away team (league)” into a structured research report for betting-direction analysis, Asian handicap, over/under, score selection, and multi-match combinations.

How It Works

• Input parsing: extracts home team, away team, and league from natural language; infers the league when missing and marks the inference source.
• Data collection: prioritizes user-provided data, then supplements recent form, home/away splits, standings and motivation, injuries, weather, odds, Asian handicap lines, over/under markets, H2H history, and ELO / rating signals.
• Model computation: feeds the collected values into compute_models.py to generate ELO, Poisson, weighted probability tables, and value point f*, reducing manual probability drift.
• Report assembly: follows a fixed template to produce core contradiction, motivation and injury weights, model consensus and bookmaker direction, and a final decision matrix with S/A/B ratings and stake percentages.
• Delivery: writes Markdown first for review, then converts it to Word while keeping the .md source.

Boundaries And Notes

The skill is useful for turning fragmented match signals into a checkable analytical framework, but it does not replace live data sources or final betting decisions. Estimated values should be marked as confirmed or estimated, and missing data should use script defaults with a clear sample-estimate label. Outputs are for research reference only; real decisions should account for in-game variables, account risk, and compliance constraints.