Casino rating according to the aggregate assessment of experts

1) Why do you need an "expert" rating

User voices are useful, but subject to cheats and emotions. Experts are a filter of professional criteria: licenses and compliance, payments, game integrity, live stream quality, support, RG tools, UX and reputation. Cumulative assessment allows:

Reduce heterogeneous opinions into one numerical metric.
Take into account the competence of the expert in a specific criterion.
Ensure repeatability and auditability of results.

2) Expert panel: how to shape

Selection criteria: experience ≥ 3 years in the domain (regulatory, payments, live technologies, support, RG/compliance), no conflict of interest.

Quotas: at least 7-12 experts covering different domains (law/compliance, payments, live-ops, UX/A11y, data).

Declarations: NDA + declaration of affiliation; experts with conflicts of interest rate all but related brands.

Calibration: Run 3-5 reference cases together to level the scale.

3) Rubricator and weights (base model example)

Sum of weights = 1. 00.

Criterion	Weight
Licenses and compliance	0. 20
Responsible Play (RG)	0. 10
Security and payments	0. 15
Game Integrity and Auditing (RNG/Live)	0. 10
Quality of live experience (e2e, sound, folbacks)	0. 15
Support and disputes	0. 10
UX and Availability (A11y)	0. 10
Reputation and complaints (faith.) 0. 05
Transparency/Management	0. 05

💡 In regions with strict regulation, the weight of the "License" can be raised to 0. 30, and reduce "Reputation" to 0. 03.

4) Assessment scale and expert form

Each expert (e) puts a score (r_{e,k }\in [0; 100]) by public checklist (subcriteria with prompts and thresholds).

Examples of prompts:

Payments: output p95 ≤ 24 h = 90-100; 24-72 h = 70-89;> 7 days = 0-30.
Live: e2e (95p) ≤ 2. 5 c = 90–100; 2. 6–4. 0 = 70–89; >6. 0 = 0–30.
RG: limits/timeout/self-exclusion of 1-2 tapas = 90-100; no self-exclusion = ≤ 40.

5) Normalising and tackling 'generous/strict' experts

1. Standardization by expert (z-scores):

[
z_{e,k} = \frac{r_{e,k} - \mu_e}{\sigma_e+\epsilon}
]

where (\mu _ e ,\sigma _ e) is the average and RMS of all points scored by the expert (for all casinos/criteria).

2. Inverse conversion to [0; 1]:

[
s_{e,k} = \Phi(z_{e,k})
]

where (\Phi) is the standard normal CDF.

3. Emission limit: winsorize on 5-95 percentiles before standardization.

💡 Alternative: quantile ranking or robust z (MAD) if the distributions are heavy-tailed.

6) Weighing competence and reliability experts

Final weight of the expert (w_e) - mixture:

Competence in criterion (k): (c_{e,k}\in[0; 1]) (declared and confirmed by cases/portfolios).
Reliability of consent: for example, contribution through the α of Crippendorf/ κ Cohen; above agreement → above weight.
Activity and completeness: penalty for omissions> 10% marks.

Weight formula for criterion (k):

[
W_{e,k} = \lambda_1 c_{e,k} + \lambda_2 \underbrace{\text{Reliab}e}{\text{по α/κ}} + \lambda_3 \text{Coverage}e
]

(usually (\lambda _ 1 = 0. 6,\ \lambda_2=0. 3,\ \lambda_3=0. 1)), then normalize (\sum _ e W {e, k} = 1).

7) Aggregation by criterion and total casino score

1. Criterion scores:

[
S_{k} = \sum_{e} W_{e,k}, s_{e,k}
]

2. Final casino score:

[
\text{Score} = \sum_{k} \omega_k, S_{k}
]

where (\omega _ k) are the weights from the rubricator.

3. Confidence interval (bootstrap according to experts): 10k remutations → p5-p95 for Score.

8) Ranking: Sustainable practices

Weighted amount (default). Simple, transparent.

Borda rule (for pure rank). Total points by expert positions; resistant to "wound" points.

Bayesian smoothed estimate:

[
\hat{\theta}i = \frac{\sum_e w_e, r{e,i} + m\mu_0}{\sum_e w_e + m}
]

where (m) is the prior force, (\mu _ 0) is the global mean. Useful for different numbers of ratings.

Paired comparisons (BTL/Plackett-Luce). If experts rank rather than score.

9) Example of mini-calculation (3 casinos × 3 criteria × 4 experts)

Let after normalization and weighting by competence obtained (S_k):

Casino	Licenses (0. 20)	Payments (0. 15)	Live (0. 15)	Other (sum of weights 0. 50)	Score
A	0. 92	0. 78	0. 85	0. 72	(0. 92·0. 20 + 0. 78·0. 15 + 0. 85·0. 15 + 0. 72·0. 50 = \mathbf{0. 79})
B	0. 88	0. 82	0. 73	0. 76	0. 78
C	0. 80	0. 76	0. 88	0. 68	0. 75

💡 Discrepancies A vs B below 0. 02 - show them as a draw within the margin of error if the confidence intervals intersect.

10) Reliability and consistency of experts

Krippendorf α (universal for interval scales): ≥ 0. 8 - excellent; 0. 67–0. 8 - acceptable; below - revision of headings/calibration.

Cohen/Fliss κ - if the scale is discrete.

Rater drift: compare the early/late half of the questionnaires; at drift - recalibration, expert weight reduction.

11) Anti-manipulation measures

Blind assessment: experts do not see other people's points and the branding of the "customer."

Randomizing the order of casino cards.

Conflict control: auto-exclude expert from related brands.

Anomalies: Grubbs/ESD emissions test for each criterion; sharp discrepancies → manual verification.

Edit history: Any change after the fact is recorded in the changelog with a reason.

12) Publication transparency

Methodology: public weights, formulas, update date, panel composition (without personal data - roles/experience/domains).

Casino passports: expanded cards - sources, excerpts from the rules, RG/limits screen, live quality metrics.

Errors: Publish confidence intervals and a "draw" flag.

Operator appeals: SLA response, list of acceptable documents (license, regulatory letters, audit reports).

13) Updates and rating life

Frequency: basic recalculation monthly; unscheduled - when changing the license, regulator fines, mass payment/security incidents.

Versioning: vYYYY. MM, public diff (what changed and why).

Deactivations: the casino is removed from publication if the license is "suspended" - until clarified.

14) Model extensions (when "up")

Regional sub-ratings: their weights/norms for Ontario, EU, LatAm, etc.

Multicriteria Analysis (MCDA): TOPSIS/MAUT as an alternative to simple sum.

Hybrid with RUM data: automatic live-quality metrics (e2e/startup/rebuffering) are added as an "expert sensor" with a separate weight.

Explainability: Shapley-decomposition of the contribution of criteria to the final score.

15) Frequent mistakes and how to avoid them

Mixing jurisdictions into one scale. Make regional versions.

Opaque weights. Publish and argue; changes - only through changelog.

Ignore scatter. Write confidence intervals, do not hide the "draw."

Skewing one domain. Balance the panel and use competent weights.

One expert "drags" the assessment. Limit the contribution of one rating to the caps threshold (for example, ≤ 25% in the criteria).

16) Checklists

For organizers

Panel 7-12 experts, roles/domains covered
Rubricator and weights published
Calibration on standards; α ≥ 0. 67
Normalization (z/MAD), winsorize, abatement
Competency weights (W_{e,k}) and caps by contribution
Bootstrap and confidence intervals
Changelog, appeals, casino passports

For readers

Update Date and Rating Version
Methodology and weights available
Errors and sources visible
Verification of legality in your country - mandatory

17) Casino Public Card Template (Recommended)

Final score + interval (p5-p95)

Strengths: 2-3 bullets (by criteria)

Risks/limitations: 2-3 bullets

Docking basis: license (no., regulator), RG tools, payments (p95 output), live metrics

Changes for version vYYYY. MM: what is improved/impaired

Aggregate peer review is a procedure, not "editorial taste." A clear panel, transparent weights, normalization, stable aggregation methods and publication of errors turn subjective opinions into a reliable, repeatable rating. This rating helps players choose safely and consciously, and operators understand what to improve in order to honestly raise their score.