Debate feedback is useful only when it explains why a side lost and how that side can revise to become more competitive. However, LLM-generated feedback often sounds polished while remaining generic and vague. We introduce ReFeedGraph, a graph-based method that turns prior debate feedback into reusable principles. It collects multi-framework judge feedback, extracts weaknesses and actionable suggestions, abstracts them into principles, and stores them in a graph. For a new debate, ReFeedGraph retrieves relevant principles and adapts them into targeted revision feedback. We evaluate the generated feedback by recording whether it improves the weaker side after rewriting. Across three existing datasets (DebateArt, Intelligence Squared, and British Parliamentary debates), ReFeedGraph outperforms direct LLM and structured-prompt baselines, producing more specific and actionable feedback that leads to winning more debates.