This paper presents a quantitative framework for predicting which post-disruption pivot strategies will succeed before outcomes are observable. Building on the substrate overlap methodology developed in the Radial Disruption Field framework (Paper 1), we derive a composite pivot quality score from three measurable inputs: substrate overlap between the displaced firm and the target market **(f(D,T))**, substrate distance between the disruptor and the target market **(1 - f(X,T))**, and a timing-adjusted reach factor calculated from the RDF displacement lag formula. Aggregating pivot quality across disruption cycles produces the Resilience Quotient, a firm-level measure of systematic survival capacity. The framework is validated against 31 companies across 14 disruption contexts spanning 1952 to 2025. Three structural findings emerge from the validation set. - First, companies that pivot toward targets with high disruptor adjacency consistently produce the lowest quality scores across all case-observation pairs.- Second, markets protected by regulatory certification, professional licensing, or liability constraints produce the highest and most durable quality scores, because these barriers cannot be overcome by technical improvement alone.- Third, pivot timing is multiplicative: companies initiating pivots before observable displacement signals achieve reach factors two to three times larger than companies initiating equivalent pivots after displacement onset. The pivot quality formula produces complete rank separation between surviving and non-surviving firms across all 30 in-sample cases, with a 0.080 gap between the highest-scoring failure (RQ = 0.090) and the lowest-scoring survivor (RQ = 0.169). A companion validation study extending the dataset to 59 companies across 7 independent disruption categories — telecom, retail, print media, broadcast, music, travel, and local directories — confirms clean separation at n=59 (min survivor RQ = 0.166, max failure RQ = 0.090, gap = 0.076). Applying Firth’s penalized logistic regression to the expanded dataset, given complete separation, yields β(RQ) = 46.07 (SE = 16.86, p = 0.006), with a survival threshold of RQ = 0.127 (95% CI: 0.082–0.172). These results should be read as scoring validation, not independent predictive accuracy claims. All cases were scored after outcomes were known. Out-of-sample validation is currently running on prospective predictions for AI coding tools and Google Search, with partial outcomes observable as of 2025–2026. The framework addresses a gap in the existing disruption literature. Christensen’s disruptive innovation model predicts how incumbents fail against disruption but does not generate quantitative predictions about which post-disruption destinations are viable. The Ansoff product-market matrix provides a taxonomy for growth directions without accounting for the disruptor’s simultaneous adjacency to those same destinations. The Barney resource-based view identifies resources as the basis for competitive advantage but does not specify how disruptor proximity changes which resources remain durable. The pivot quality score operationalizes each of these frameworks for the post-disruption context.