Kenya audit checklist · EAT + FKF
Audit a football prediction site in Kenya
Audit checklist: prove what a site published, when it published it and what happened to every eligible prediction. Apply the EAT and FKF checks below to the combined seven-site public-evidence dataset; that dataset owns the provider list and brand-by-brand evidence comparison.
Published by Football Proof AI · Published · Updated · Non-ranking educational guide, not betting adviceLocal clock · Exact instants
Convert every publication and kick-off to EAT
East Africa Time is UTC+3. The same instant can carry a different calendar date in EAT and UTC, so a screenshot saying “today” or “tomorrow” cannot establish lead time by itself.
Demand a zone or offset
Preserve an ISO 8601 instant such as a UTC value ending in Z or a local value with +03:00. A clock time without a zone is ambiguous.
Compare like with like
Convert scheduled kick-off to EAT, then calculate the lead time. Do not compare an EAT publication clock with an unlabelled UTC fixture clock.
Do not trust relative dates
At 21:00 UTC the EAT calendar is already on the next day. Record the exact instant instead of using “tonight” or a page heading as timestamp proof.
FKF Premier League · Fixture continuity
Keep the prediction attached to the correct match
Team names and a visible date are not a durable fixture key. For FKF Premier League evidence, the archive should preserve enough identity and revision history to connect the original forecast to the correct final result without rewriting the prediction.
- 01
Identify the competition
Record the exact league, season, home team, away team and stable provider or publisher fixture ID.
- 02
Preserve the scheduled time
Keep the kick-off known when the forecast was published instead of replacing it silently with a later schedule.
- 03
Append revisions
If a fixture is postponed, abandoned or corrected, add a dated status event and retain the original record.
- 04
Settle from a final source
Join the final score to the same fixture identity and disclose the settlement rule; do not infer a result from a changed page title.
The open prediction evidence standard shows how a locked forecast and append-only settlement can remain separate. Conformance does not by itself prove historical publication or model accuracy.
Loss-first audit
Look for misses before reading testimonials
A winning example is easy to publish after selection. A complete archive is harder: it must retain losing rows, explain non-settled fixtures and allow the advertised denominator to be rebuilt.
- 01
Open past dates
Move through several declared forecast days rather than following a curated “recent winners” link.
- 02
Find losing rows
Confirm that misses remain visible with the same fields and stable URLs as wins.
- 03
Reconcile states
Count settled wins, settled losses, void, postponed, cancelled and pending rows separately.
- 04
Rebuild the rate
Divide correct predictions by all eligible settled rows under the declared rule and compare it with the headline.
A percentage without correct and settled counts is not reproducible. The PROOF-5 claim checker recalculates a supplied hit rate and exposes its sample uncertainty; it does not independently fetch or authenticate the provider's archive.
Forecast quality · Full distribution
A probability is more informative than a “sure” tip
The top 1X2 outcome is only one part of a forecast. Keep the home, draw and away probabilities together so the prediction can be checked for arithmetic, scored after the result and audited for calibration over time.
- Complete split
- Home, draw and away values from the same model run, summing to approximately 100% after stated rounding
- Top-pick rule
- A declared treatment of exact ties rather than silently forcing one outcome
- Brier convention
- Whether the three squared errors are summed, averaged or otherwise scaled before scores are compared
- Calibration view
- Observed frequencies for sufficiently supported probability ranges, with sample counts and bin choices visible
- Market boundary
- 1X2 results kept separate from BTTS, totals, double chance and correct-score calls
- Uncertainty language
- No percentage described as a guarantee, certainty or evidence that the next forecast must win
Use the probability-reading guide to interpret a complete split, fair odds and calibration without turning uncertainty into certainty.
Sample scope · Kenya must be defined
Make every performance denominator answerable
A site may address Kenyan readers while evaluating matches from many countries. That audience choice does not make a global sample an FKF Premier League sample. Require the claim to define exactly which rows it includes.
Which fixtures qualified?
Record leagues, seasons, markets, confidence filters and publication lead-time rules before reading results.
When did evaluation start and end?
A rolling week, season and lifetime history are different samples. The page should name the date range and update rule.
Which rows did not count?
Postponements and invalid data can be excluded under a fixed policy, but unfavourable outcomes cannot disappear after settlement.
Method, editorial layer and disclosure
Find out what produced the number and what promotes it
“AI-powered” does not identify a model. A public method should describe the forecast target, input cutoff, versioning and out-of-sample evaluation. An AI or human tipster may explain a locked probability, but it should not alter that number or invent team evidence after publication.
- 01
Model identity
Look for a version, training cutoff, feature boundary and status rather than a permanent generic “AI” label.
- 02
As-of inputs
Tables, form, team news, line-ups or odds must have been available before the forecast timestamp.
- 03
Forward validation
Evaluation should respect time and compare the model with a named baseline on the same rows.
- 04
Commercial disclosure
Subscriptions, advertising and affiliate relationships should be visible enough to evaluate the incentive behind a promoted action.
Apply the checklist · Keep one evidence source
Use the seven-site dataset as the provider evidence source
The shared Nigeria-and-Kenya index owns the named provider list, comparison matrix and cited source dossiers. This country page supplies the EAT and FKF audit procedure only, so the underlying observations have one canonical source instead of seven repeated profile lists.
Interpretation boundary
No availability check predicts profit or safety
A public archive, timestamp, method or disclosure is useful evidence about transparency. It is not proof of accuracy, calibration, trustworthiness, legal status, product quality, safety or profitability. “Not publicly located” is a dated null value, not evidence that a provider cannot supply the item.