What GC Score means
Gambling Charts Popularity Score (GC Score) is a normalized 0–100 popularity score inside a specific ranking. It is designed to answer a narrow question: which eligible brands appear most popular according to the comparable signals validated for that market and category?
GC Score is not a quality score. It is not market share, a safety rating, a prediction of winnings or a universal score that should be compared blindly across different gambling categories.
One framework, category-specific evidence
We do not assume that the same data mix works equally well everywhere. Each market and category is researched separately. A signal is used only when its coverage, attribution and comparability are strong enough for that ranking.
Start with eligibility
Define the relevant licensed/current brand universe before scoring so the ranking is not just a list of commercial partners.
Validate comparability
Check whether popularity inputs cover brands consistently and actually represent the brand being ranked.
Normalize transparently
Comparable signals are normalized within the ranking and combined according to the published profile.
Do not invent precision
Brands without enough comparable evidence can remain visible without receiving a misleading score.
Current UK Sports Betting profile
The current Great Britain sports-betting chart uses the frozen UK-BETTING-GPI-v1.0 profile. For brands with sufficient comparable data, GC Score combines two normalized signals at equal weight:
- 50% web popularity — Similarweb-derived web audience estimates after brand/entity QA.
- 50% branded Google search demand — branded search volumes from Google Keyword Planner after query QA.
In the current dataset, 78 brands receive a score and 16 eligible brands remain visible without a score because comparable evidence is insufficient. Their missing data is not treated as zero.
Similarweb is one signal, not ground truth. Traffic estimates can be noisy or incomplete. That is why we combine signals, validate entities and keep uncertainty visible instead of presenting third-party estimates as exact audience counts.
Current UK Online Casinos profile
The current Great Britain online-casino chart uses the frozen UK-CASINO-GPI-v1.0 profile. Casino evidence was tested separately rather than inheriting the sportsbook formula. The current score uses one comparable popularity signal: attributable branded Google search demand.
- One semantically attributable branded query per brand — alternative query variants are diagnostic and are never summed to inflate demand.
- Search normalization — comparable raw demand is transformed with log1p and then min-max normalized to the ranking’s 0–100 GC Score scale.
- Measured zero stays zero — a defensible query that returns zero demand is not treated as missing, but zero-demand brands are not given arbitrary ordinal positions against one another.
- Missing stays missing — genuinely non-comparable or unattributable evidence is left unscored rather than forced to zero.
The current 248-brand market universe contains 222 brands with positive comparable demand, 25 attributable measured-zero brands and one limited-data brand. The domain-level Web signal tested for this category was excluded because differences in root domains, paths and product architecture made the current measurement rule materially non-comparable. We do not add a weak second signal just to make the formula look more complex.
Trend is separate from GC Score
The Trend badge on the UK betting ranking shows the latest month-on-month change in estimated web popularity. It is contextual only: Trend does not change GC Score or ranking order. A short-term spike should not silently rewrite the core popularity chart. The current UK casino ranking does not invent a separate Trend where an equivalent defensible signal has not been frozen.
Commercial independence
Affiliate status, commission rate, paid placement or other commercial relationships do not determine eligibility, GC Score or ranking order. If commercial links are introduced, they must be disclosed separately from the ranking methodology.
Editorial facts are a different layer
Product features, payments, customer-funds information and other consumer context can help people compare highly ranked brands, but these facts do not become hidden scoring inputs. Popularity decides the order; editorial context explains what the brands offer.
Updates and changes
Ranking data should be refreshed on a defined schedule, while methodology changes require a reason that is stronger than a single surprising result. If a profile materially changes, the change should be documented rather than silently rewriting the historical meaning of the score.