RANKING
How the Top Rated list is sorted, and why a 5.0 with 2 reviews doesn't always beat a 4.8 with 10.
The problem
A simple average rewards luck. Three perfect 5★ reviews give a 5.0, but that's a tiny sample — one bad review would tank it. A game with a 4.8 average over 40 reviews is a stronger signal of quality, even though the raw number is lower.
Sorting by raw average puts low-volume games on top by accident. Sorting by review count alone ignores quality. We want both.
Bayesian average
Top Rated uses a Bayesian average — every game is treated as if it had a few extra phantom votes at a neutral rating. Games with many real reviews barely feel the prior; games with very few are pulled toward the neutral score.
With k = 5 and m = 4.0, the prior is moderate. A game with very few reviews is pulled noticeably toward the neutral score; once a game crosses ~15+ reviews, the score is mostly the real average. This stops a 5.0 with 2 reviews from squatting at #1 while still letting genuinely better-rated games beat noisier ones.
Live example
The current top 5 reviewed games on the site, scored under the formula above:
| # | Game | Avg | Reviews | Score |
|---|---|---|---|---|
| 1 | Hollowlands | 4.6 | 289 | 4.59 |
| 2 | Undersphere | 4.6 | 69 | 4.56 |
| 3 | A Game About Capybaras Delivering Food | 4.6 | 65 | 4.56 |
| 4 | Second Form: Requiem | 4.4 | 21 | 4.32 |
| 5 | Hip To Be Square | 4.3 | 15 | 4.22 |
The displayed star average stays the real number — only the internal ranking score is weighted.
One author, one vote
Anyone can post as many reviews as they want — but for the rating that matters (the average shown on the game page), all reviews from the same author are collapsed into a single vote using their own internal average. So six 5★ reviews from one person count the same as one 5★ review.
This means the rating is resistant to spam: posting the same review ten times doesn't move the needle. It also means a single determined troll can't tank a game by spamming 1★ — they get one 1★ vote, not ten.
On the game page, multi-review authors show up as a single card with the average of their own ratings up top, the most recent review below, and a "Show N previous reviews" expander that opens a tree view of the rest.
Identifying authors
Anonymity is a product requirement — there's no login. To identify the same person across reviews without an account, three signals are combined, in order of strength:
- Device fingerprint — a SHA-256 hash of canvas rendering, WebGL renderer/vendor, OfflineAudioContext output, installed font probes, screen, timezone and user-agent. Stable across cookie clears, browser restarts, and most VPN switches.
- Anonymous client cookie — a random UUID stored in an
httpOnlycookie (vr_uid) for one year. Survives IP changes; cleared when the user wipes cookies. - IP hash — SHA-256 of the request IP plus a server secret. Raw IPs are never stored. Last-resort signal — shared networks (campus Wi-Fi, mobile carriers) collapse onto one hash.
For grouping, the strongest available signal wins: fingerprint → cookie → ip. Each review carries an opaque, truncated fingerprint id (e.g. #a1b2c3d4) shown on the card so repeat authors are visible at a glance.
Tiebreakers
- Higher Bayesian score wins.
- If scores tie, more reviews wins.
- If review counts also tie, higher raw average wins.
Other sorts
- Newest— sorts by the game's submission timestamp. Reviews don't affect this.
- Most Reviewed — pure review count, with average as the tiebreaker.
Card rank numbers
The #1, #2… badges on each card reflect the position in the currently visible list, after filters and the active sort have been applied. Switch from Top Rated to Newest and the numbers renumber to match what you actually see — they're a position indicator, not a permanent badge.
Star display
Stars use fractional fills — a 4.5 average shows four-and-a-half stars filled, not five. The numeric score (e.g. 4.8 (4)) is shown next to the stars so close averages are easy to tell apart.