Casino rating based on reviews of real players

1) What counts as a "real recall"

Confirmation of facts: screen/ticket number, time of request for output, amount/method, support response.
Specifics without general phrases: "output 300 AUD via PayID in 2 hours"> "everything is ok."
Verifiability: date, currency/method, country, game is not fundamental, but the policy of payments/bonuses is important.
Neutral/negative cases without emotion are valued above "5 for beautiful design."

Exclude: templates of the same type, promo codes/reflinks, "rain" of reviews in one day, accounts ≤7 days, mass 5 without details.

2) Sources and weight (in order of priority)

1. Platform with verification of players/cases (screenshots, tickets, public moderation) - weight × 1. 0.
2. Thematic forums/communities with active moderation - weight × 0. 8.
3. Feedback aggregators without verification - weight × 0. 5.
4. Social networks (raw data) - weight × 0. 3, use only in bulk.

Rule: always keep the link/source ID and "evidence" (screen/ticket).

3) Normalization of estimates

Scales → 0-100: 5 = × 20, 10-point = × 10, Yes/No → 100/0.
We highlight the categories separately: "Output speed," "KYC/SoF," "Bonuses," "Support," "Honesty/games," "UX/mobile."
Recall categorization: A single record can fill in multiple metrics (for example, both "output" and "KYC").

4) Anti-cheating (minimum)

Attribute deduplication: IP/time/text ~ 80% match, new account/day packs - flag.
Linguistics: pattern repeatability, same caveats/emojis, unnatural frequency 5.
Time anomalies: surge per site at 24-48 hours → manual check.
Polarity balance: when accelerating 5 without specifics, we reduce the weight.
Cross check: feedback on "instant output" with real SLA 24-48 h - manual parsing.

5) Smoothing and "honest" overall evaluation

Bayesian smoothing (for mean):

• Adjusted = (v/(v+m))·R + (m/(v+m))·C,
• where * R is the average rating of the casino, * v is the number of reviews, * C is the overall average for the market (for example, 3. 7 → 74/100), * m - minimum volume (for example, 100 reviews).

Wilson's lower bound (for the fraction of "positive"):

• use on key metrics (for example, "output on time") - protects against a small sample.

Age: exponential decay with a half-life of 180 days (fresh reviews are more important than old ones).

6) Rating metrics (weight in 100-point system)

1. Payouts/SLA - 25 points

% of conclusions in the declared SLA (Wilson weight).
Share of cases "> SLA + no answer" (penalty).
Presence of same-method, absence of "paid priority."
2. Disputes/Resolutions - 20 points

% successfully closed complaints, median resolution time.
Presence of ADR/mediator, public log of cases.
3. KYC/SoF - 15 points

Transparent lists of documents, cases "KYC only after winning" (penalty).
Share "unreasonable refusal/freezing."
4. Bonuses/T & C - 15 points

Clarity WR/max bet/cap, contribution of games before bonus activation.
No "deposit turnover × 2- × 3" without bonus.
5. Honesty/Content - 10 points

Official RGS domains, RTP/Paytable match in Info with provider.
6. Support - 10 points

Time to first response (chat/e-mail), competence, escalation.
7. UX/mobile - 5 points

Lobby ≤3 -5 s, slot ≤10 -15 s, stable FPS, understandable HUD.

Formula: Total = Σ (normalized metric × its weight). Minimum thresholds for each metric can be entered as "barriers" (if failed, the overall rating from above is "cut").

7) How to consider "bad but honest" cases

We fine systemic violations (massive delays/failures), and not single disputes with argumentation.
If the operator publicly corrected practices (announced SLA/updated T&C) and metrics improved 90 + days - remove time penalties gradually (fading).

8) 15-minute checklist before adding casino to rating

1. License/registry (2 min): number of → click in the registry; legal entity/domain matched.
2. Payouts (3 min): published methods, min/max, fees, SLAs for each method; is there a same-method.
3. T & C/bonuses (3 min): WR/max bet/cap/contribution of games on one page; no "deposit turnover × 2- × 3" without bonus.
4. Originality of games (3 min): RTP/modes are visible in the Info slot; Match the provider download domains are official.
5. Reviews (4 min): 10-20 fresh cases with evidence; filter out obvious cheats; Review CCR complaints/findings.

9) Red flags (immediately "minus in the rating")

There is no click on the license registry/in the registry of another legal entity/domain.
Mass complaints "withdrawal> SLA + silence," "paid priority of withdrawal."
Deposit turnover × 2- × 3 without bonus, "restricted" hidden lists.
RTP/Paytable mismatch with the provider; loading from "left" domains.
A wave of the same type of 5 reviews per day with the same wording/without numbers.
Closing complaints without argumentation ("discretionary"), retroactive rule changes.

10) Card template for collecting feedback (fill in for each case)

Source ID/Reference:

Date/Country/Currency/Method:
• Case type: output/KYC/bonus/support/other
• Facts: amount, request time, promised SLA, actual time, support response, ticket/screen
• Rating by category (0-100): payouts/KYC/bonus/support/honesty
Final score (0-100):
• Anomaly labels: template/reflink/no numbers/new account

11) Calculation example (simplified)

Reviews on "payments": 160 cases, 124 in SLA → p̂ = 0.775; Wilson (95%) lower bound ≈ 0.71 → 71/100 on "disbursements."
KYC: 60 cases, 9 disputed delays → 85/100.
Bonuses/T & S: 70/100 (there were complaints about hidden cap in FS).
Support: median response 3 min in chat → 88/100.
Honesty/content: RTP match/domains - 100/100.
UX/mobile: 80/100.
Result: 0.25· 71 + 0.20· (dispute resolution, say 82) + 0.15· 85 + 0.15· 70 + 0.10· 100 + 0.10· 88 + 0.05· 80 ≈ 80/100.

12) Frequent questions

Can you trust aggregators? As one of the sources - yes, but with a reduced weight and cheat filters.
What is more important: the quantity or "quality" of reviews? Quality: verification of facts + prescription. Quantity influences through Bayes/Wilson.
What to do with VIP cases? Count separately (often another SLA), do not mix with regular ones.
Do we need to consider geo? Yes: methods/regulation are different; keep the country/currency/method.

13) Casino Review Score Matrix (100 points)

Payouts/SLA - 25
Disputes/Decisions - 20
KYC/SoF — 15
Bonuses/T & C - 15
Honesty/Content (RTP/Domains) - 10
Support - 10
UX/Mobile - 5

Interpretation: ≥85 is a strong recommendation; 75-84 - fit; 60-74 - average; <60 - avoid.

Result

An honest "feedback rating" is based on verified cases, anti-cheating, smoothing small samples (Bayes/Wilson) and the weights of key metrics: payments, disputes, KYC, bonuses, content honesty, support and mobile quality. Use a 15-minute checklist, 100-point matrix and case card - and you'll get a rating that reflects the actual user experience, not marketing.