Hype,
quantified.
An AI-powered market index: SoleSight fuses live search demand and machine-learning 30-day forecasts with resale, social and transformer-read community sentiment into one Hype Score for every silhouette it tracks — each signal labeled for exactly what it is.
AI consumer intelligence for sneaker brands, retailers and merchandising teams: spot rising silhouettes, time marketing, guide inventory bets with predictive analytics.
The Index
This week's movers
Launch radar
Demand events detected straight from the data — days where a model's search interest spiked to 3×+ its trailing 90-day baseline — plus where every model sits in its hype lifecycle right now.
Market intelligence
The analyst view — every brand and category in the index, rolled up: average hype, resale premium over retail, search momentum and share of the top 10.
Case study: catching a riser
How the score works
Each signal is labeled for what it is today — live real data refreshed nightly, api-ready real adapters awaiting free API keys (demo data until then), or partial some platforms real, the rest modeled, or modeled synthesized from real search momentum pending a viable API.
Resale premium
Median ask vs. retail from live eBay listings, nightly — the market's dollar vote.
Search momentum
Google Trends interest, last 14 days vs. the prior 14 — movement within each model's own history.
Social buzz
Bluesky and YouTube live; Instagram/TikTok modeled (no viable APIs).
Search intensity
Current interest relative to that model's own historical peak — never compared across models.
Community mood
Transformer sentiment over live Bluesky chatter; Reddit joins with a free key.
Technical methodology — formula, normalization & a worked example
The formula
score = Σ(wᵢ · vᵢ) / Σ(wᵢ) over available signals
Every component maps onto 0–100 before weighting:
- Search intensity — Google's 0–100 index, 14-day mean. Relative to each model's own peak, so it measures how hot the model is vs. itself, not raw volume across models.
- Search momentum — recent 14d vs. prior 14d percent change, mapped as
50 + Δ% × 0.8, clamped to [0, 100]. Saturates at roughly ±62%. - Resale premium — median price ÷ retail MSRP, mapped linearly from 0.8× (→ 0) to 2.6× (→ 100). The clamp caps extreme listings; prices are decile-trimmed before the median to drop fakes and typo listings. A model with no known retail price contributes nothing (see missing signals).
- Social buzz — daily cross-platform engagement, normalized 0–100 to the model's own peak (same convention as Trends).
- Community mood — per-post transformer sentiment
P(pos) − P(neg)∈ [−1, 1], averaged over all scored posts, mapped to 0–100.
Missing signals: weights renormalize over whatever is present — a model missing resale data is scored fairly on the rest, never implicitly zeroed. Update cadence: search & forecasts nightly (stalest-first); other signals refresh whenever their source runs. Baseline window: each model carries ~269 days of daily history.