06 / Consumer dataNational ranking systemLive

The Canadian Shawarma Report

Somewhere in this country there is a better shawarma than the one you were about to order.

673 locations and 782,946 Google reviews across six Canadian markets, scored with one transparent methodology — not star averages, not vibes. Published as a standalone site with per-market ranked tables.

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Top of the London table.

London is the report's home market: 59 locations resolved, 57 ranked, 54,936 reviews read. The ranking is the artifact — this is the current top five, by report score.

  1. 01Mr. Shawarma & ChickenErnest Avenue · 1,291 reviews · “garlic-head approved”8.20
  2. 02Wow ShawarmaClarence Street · 809 reviews8.14
  3. 03Saj & TanoorWharncliffe South · 290 reviews8.12
  4. 04Fadi’s ShawarmaOxford East · 339 reviews8.11
  5. 05Shawarma HouseCommissioners West · 829 reviews8.05

Scores are on a 0–10 scale from the review-scoring model below, not the Google star average. Full London table and five other markets on shawarmareport.ca ↗ opens in a new tab.

Change the question, the podium changes.

Greater Toronto Area overall leaderboard: a ranked table of shawarma restaurants with Google-review counts and 0–10 report scores, led by Ghazale on College at 8.62.
The Greater Toronto Area overall leaderboard — the largest of the six markets, led by Ghazale on College at 8.62. Every list (food, portion, value, affordability, garlic sauce, review volume) is generated from the same review dataset; ask a different question and the ranking reorders. Browse every market ↗ opens in a new tab · captured September 2026.

Six markets.

Locations resolved and reviewed per market. “Ranked” excludes locations below the confidence threshold, which are listed as provisional or unscored.
MarketResolvedRankedReviews read
Greater Toronto Area319306409,349
Calgary7868111,771
Ottawa–Gatineau10410291,236
Montréal694860,438
Waterloo Region444355,216
London595754,936
Total673624782,946

Review total is the sum of each location's downloaded review count across the six per-market datasets. Montréal coverage is scoped to the City of Montréal proper.

The scoring model.

A pipeline resolves each candidate location, pulls its Google reviews, and runs them through one scoring model: a Bayesian rating with recency weighting, a per-location review cap, a text component derived from review language, and a volume score. Each location gets a 0–10 score, a market rank, and a Toumometer rating.

  • Rating

    A Bayesian score on a 0–10 scale with a prior, so a handful of five-star reviews does not outrank a well-established restaurant with thousands of reviews.

  • Recency

    Newer reviews carry more weight; a single review is capped so it cannot swing a location on its own.

  • Text

    Most of the score comes from review language — what people actually say about the food — rather than the star average.

  • Volume

    A separate volume score rewards locations with enough reviews to be confident about.

  • Ranking

    Locations are ranked within their market; those below the confidence threshold are listed as provisional or unscored, not ranked.

Source.