How Goodreads broke book reviews

By Ryan Siegal · Founder and Principal

Published 2026-04-23

The three failures

1. The 1-5 scale is too coarse for books

Books have enormous quality variance. A great novel might resonate with a reader for decades; a mediocre one is forgotten by the next weekend. Compressing that variance into five discrete buckets erases most of the information a reader would need to compare two books.

Amazon uses the same 1-5 scale and has comparable inflation problems, but books are worse because the top three buckets ("it was amazing" = 5, "really liked it" = 4, "liked it" = 3) all feel socially acceptable, while the bottom two ("it was OK" = 2, "didn't like it" = 1) carry a reputational cost most reviewers avoid. Academic studies on the platform have found that > 85% of reviews are 3+ stars and > 60% are 4+ stars across the full catalog.

2. Friend-network voting amplifies positive bias

Unlike Amazon (reviews are anonymous-ish and transactional) or Letterboxd (reviews are read by a cinephile peer group), Goodreads shows reviews to the reviewer's literal friend network first. A 1-star review on Goodreads is visible to your book-club. A Kirkus critic panning a novel is a professional opinion; a reader panning it is an awkward conversation at next month's meeting.

The incentive structure pushes readers toward DNF ("did not finish") and no-review for books they dislike, rather than a 1-star review. The negative tail of the rating distribution is artificially suppressed.

3. Readers pre-select into books they expect to enjoy

Unlike headphones or hotels, which people buy out of necessity, readers choose books they already expect to like — often on the recommendation of a friend, a trusted list, a BookTok/BookTube influencer, or the book's own cover and premise. The reader base for any given book is a positively-selected population before the first page is read.

This is different from Amazon's electronics sections, where people buy products because they need a laptop, regardless of whether they'll love it. On Goodreads, the readers most likely to log a book are the readers most predisposed to enjoy it. Self-selection bias is baked into the platform.

Why StoryGraph, Hardcover, Fable didn't fix it

A wave of post-2020 Goodreads alternatives have tried to address user-experience failures (bad search, ugly UI, no privacy) but none have changed the underlying scoring mechanics. They're mostly using 1-5 or 1-10 scales with friend-visibility and self-selection biases intact. Same inflation problem, different UI skin.

What actually works: normalize every reviewer against themselves

The right approach for book reviews reads every reviewer it can reach — professional editorial sources (NYT Book Review, Kirkus, Publishers Weekly, Booklist, LitHub, LA Times) and crowd platforms (Goodreads, Amazon) alike — subtracts each reviewer's own average and divides by their own spread, penalizes thin-sample books rather than flattering them, and ranks against a per-genre peer set.

Rankquant reads the crowd half of that: Goodreads and Amazon, with every qualifying reviewer normalized against their own rating history and counted equally. No book-trade publication is ingested — and if one ever were, it would enter on exactly the same terms. No source and no reviewer gets a bigger vote than any other.

Normalizing this way inside genre peer sets (contemporary literary, historical fiction, fantasy, memoir, etc.) recovers the signal that a raw Goodreads average throws away. A book that's 4.3 on Goodreads might land at 2.8 normalized (below-average for its genre despite high raw score) or at 4.7 normalized (genuinely exceptional). The number actually tells you something.

A book's normalized score only means something if we show you what we compared it against. Every review page exposes the peer set — "Normalized within: contemporary literary fiction, 2020-2024, hardcover-first (2,147 books)" — so you can judge the comparison.

Related: Rating inflation explained · The 7 review sources that dominate every category