About Rankquant

By Ryan Siegal · Founder and Principal

Published 2026-04-23·Last updated 2026-04-27

The rating-inflation problem

Open Amazon. Every product is 4.4. Open Yelp. Every restaurant is 4.2. Open Wine Spectator. Every "classic" wine is 92–97. Open Booking.com. Every hotel is 8.4. Signal is dead across every review surface on the internet.

If "excellent" applies to everything, it applies to nothing.

We think reviews should help you make real buying decisions. Averages don't.

Our fix: a normalized percentile, not another average

Rankquant takes raw ratings from the usual sources — professional critics, verified-purchase aggregators, enthusiast platforms — and runs them through a four-step statistical pipeline that's standard in professional practice but has never been published as the basis of a consumer review site:

1. Per-reviewer z-score normalization. For every reviewer we compute their personal mean μ_u and standard deviation σ_u, then convert each of their ratings to z_u,i = (r_u,i − μ_u) / σ_u. Personal scale washes out; what's left is each reviewer's opinion of relative quality.
2. Three aggregation lenses per product. R1 (pure relative, every qualifying reviewer equal — headline), R2 (source-weighted by published credibility), R3 (broadened to include consistent-rater reviewers via imputed σ). We publish all three because they answer different questions.
3. 90% CI-floor ranking.We rank on Ẑ − 1.645·SE(Ẑ) rather than the raw mean. A 4-reviewer +2.1 mean loses to an 80-reviewer +1.6 mean because the thin sample's SE is much wider. Small samples can't fake-rank.
4. Empirical-CDF percentile.Every product's CI-floor is ranked against every other product's CI-floor in the database and expressed as a 0–100 percentile. A 90 means top 10%. Cohort percentile is the same CI-floor re-ranked within same-category, ±20%-price peers.

Why we start with wines

Wine criticism has the longest-running quantitative scoring culture in any consumer category — Robert Parker's 100-point scale has been around since 1978. It's also the most inflated: the effective range is 85–100, compressing what should be 50 points into 15. Forty years of grade inflation have destroyed the signal.

If our methodology can cut through 40 years of wine-score inflation, it can cut through anything. Wines are the proof-of-concept. Movies, books, Amazon products, hotels, and restaurants are next. Eventually every major consumer category.

Why this is better

Consider buying noise-cancelling headphones. Sony WH-1000XM6 averages 4.5 on Amazon. Bose QuietComfort Ultra averages 4.5 on Amazon. Tie. Useless.

On Rankquant:

• Sony WH-1000XM6 → R1 global percentile 97 (Ẑ = +1.75, 142 qualifying reviewers)
• Bose QuietComfort Ultra → R1 global percentile 86 (Ẑ = +1.05, 128 qualifying reviewers)

Now you have a decision. Both are great products, but one is measurably better relative to the category. That's the information buried in the raw data that averaging throws away.

Now consider wines. 2019 Louis Jadot Bourgogne Chardonnay scores 89 on Wine Spectator. So do 400 other wines. Useless. On Rankquant it sits at global 62 and cohort 91within its same-category $15–$30 cohort — not a standout globally, but a best-in-price-class pick. That's a real buying signal.

Why you should trust our logic

Rankquant is led by Ryan Siegal, whose fifteen-year quantitative-finance career is the source of the methodology. Ryan founded a multi-strategy hedge fund (14% average annual return, $12M AUM), traded event-driven and merger-arbitrage strategies at Alpine Global with 25–35% annual returns, and now applies Python and AI/ML to research and systematic process improvement at Relentless Upside Consulting. Washington University in St. Louis alum. Consulting statisticians Ben, Josh, and Yang contribute STEM-trained input on distribution analysis, source-weight calibration, and outlier detection.

The methodology behind Rankquant — z-scoring, confidence intervals, weighted-mean aggregation — is the same statistical toolkit that runs trading systems. None of the math is novel. What's different is publishing it openly, applying it rigorously, and committing to constants up front so the output is reproducible. We don't claim novel math. We claim applied math — taking methods that work in finance and psychometrics every day, and using them on review data where the field has been making do with raw averages for thirty years.

Everything we do is documented at /methodology. Every review page shows the math step-by-step: raw scores per source, the formula, the intermediate values, the final score. If you disagree with the output, you can check our work. The normalization source code is published at github.com/rankquant — fork it, improve it, cite it.

Independence and disclosure

Rankquant earns commissions on purchases made through retailer links. Our primary "Buy" link routes to the retailer that combines lowest observed price with highest affiliate commission. The routing logic is published at /methodology#affiliate. Commissions never affect the normalized score. Brands cannot buy a higher ranking.

How to use this site

If you're buying something right now:

1. Go to the category (/wines; later /movies, /books, /headphones, etc.).
2. Look at the top 5 by normalized score.
3. Read the methodology-transparent breakdown on the product page.
4. Click through to the retailer we've selected as best-for-you.

If you're a brand or researcher: per-category distributions will be published monthly at /data. The normalization code is open-source.

If you have feedback:every review page has a "report a correction" link. We take methodology criticism seriously. If you spot a statistical error we'll fix it, publicly, and attribute you.

Our editorial policy

• No product is listed without at least 50 real reviews across at least 2 sources.
• Sponsored content is never re-ranked. Brand partnerships are labeled clearly.
• Categories are defined by our editorial team; we do not split or merge categories to benefit any specific product.
• Our top-pick logic is reproducible: publish the same raw inputs on the same date, get the same output.
• Historical scores stay visible as data evolves.

Based in New York. Content is verified and re-checked monthly.