Designing a runway look is a problem with data inputs and an objective function. First, gather trend curves from last 24 months, filter out spikes that are likely noise, then compute a weighted average of color frequencies. Next, run a sentiment analysis on the top 500 posts with the hashtag, normalize the scores to a 0–1 scale, and treat high sentiment as a multiplier for the color weight. Finally, run a linear regression between past best‑selling looks and their launch metrics; the coefficients tell you how much each variable contributes to the “jaw‑dropping” score. Adjust the palette, silhouette, and fabric until the model predicts the maximum score. That’s the statistically flawless runway you asked for.