This study presents an integrated pipeline that transforms anomaly-detection outputs from global fitness-market data into data-faithful narratives. Using a 132-country panel and a 2019 baseline, we analyze observed post-COVID-19 recovery through 2025, while treating 2026 only as a modelled extension. Four complementary detectors—regional-median deviations, linear-regression residuals, Random Forest residuals, and Isolation Forest—are combined through method agreement and mapped to a rule-based typology. A deterministic, template-based Natural Language Generation (NLG) component then produces short, medium, and complete explanations from tabular evidence. In 2025, 14 countries were consensus anomalies; Guyana exemplified market growth without participatory expansion, with revenue recovery of +401.43%, participation recovery of -2.06%, and a 403.50-point gap. All 132 narratives passed automatic factual-consistency checks. These checks validate field preservation rather than fluency, usefulness, or explanatory quality; human evaluation remains future work.