Texas Powerball Generator

Draw Date

Number of Draws

Historical Data

Visible

Bypass Historical Data

INSUFFICIENT DATA Direct Fetch (auto) Years: to

drawCount: 0 | lastJackpotWinDate: unknown | mostRecent: unknown

Enhancement #10 provides deterministic Powerball.net draw-history loading to enable Jackpot Event Forecast (JEF). If direct fetch is blocked by browser policy, save Powerball.net archive pages (e.g., /archive/YYYY) and import them.

Prediction Method:

Exponential Frequency LSTM Monte Carlo Markov

Methodology Rationale

Visible

Why these numbers were selected (method, ranks, weights/scores).

Candidate Pool Construction

Generate a large candidate universe; score tickets; then focus on a Top-K pool (K=50) for ranking stability.
Reduces noise dominance while preserving diversity of viable tickets.
Final rankings are computed within the Top-K pool using the current scoring logic.

Frozen Reference Window

Uses a fixed 260-draw historical window as the baseline for all weighting and comparisons.
Prevents silent drift caused by changing sample sizes across runs/dates.
Improves repeatability: same inputs yield the same outputs.

Scoring, Ranking, and Agreement

Scores combine overlap signals, ranked weights, and (where enabled) cross-method agreement.
The Method Agreement Index rewards candidates supported by multiple independent methods.
Outlier patterns may be penalized to reduce overfit to rare historical artifacts.

Run Likelihood % (RL%) Interpretation

Run Likelihood % (RL%) is a percentile-style score normalized within the comparison pool for this run (not jackpot odds).
“Model Run Likelihood %” ranks within a method; “Global Run Likelihood %” ranks across methods for the same draw date.
Run Likelihood % is most meaningful when comparing tickets from the same run and draw date.

Validation Hooks and Stability Controls

Range/duplicate checks, date gating, and data-load readiness guards prevent invalid states.
History reconciliation recomputes Global Run Likelihood % across all entries for a given draw date.
Any scoring or rules change must update this Rationale to remain auditable.

Run Breakdown (click a generated game or a Drawing History row)

Generate a set (or click a result in Drawing History) to see the breakdown here.

Drawing History

Visible

Model Type	Draw Date	Numbers	Powerball	Model RL (%)	Global Run Likelihood (%)

Top Duplicates

Visible

Numbers	Powerball	Score (%)	Frequency

Exports

Analysis

Enhancements and analytical modules. Additive-only. Sections may be hidden or shown by build version.

Jackpot Data

Years

Total Hits: 0

Dataset: Not loaded

Schema: pb_jackpot_hits_vE3_2

Draw Date	Jackpot Advertised	Cash Value	Winners	Winner States	Retailer City	Retailer County	Numbers	Notes
No jackpot hit records loaded.

Pattern Analytics

Descriptive pattern analytics on the jackpot-hit dataset.

Clusters (k): Outlier sensitivity (z):

Clustering

K-means over normalized features: log(jackpot), roll draws to hit, month-of-year, winnerCount.

Sel	Cluster	Count	Avg Jackpot	Avg Cash	Avg Roll Draws	Avg Winners	Dominant Months
Run analytics to populate clustering results.

Outlier detection

Outliers flagged by z-score on log(jackpot) and growth-per-draw (when available).

Sel	Draw Date	Jackpot	Cash	Roll Draws	Growth/Draw	z(logJackpot)	z(growth/Draw)	Notes
Run analytics to populate outliers.

State representation vs expected share

Observed state “ticket hits” versus expected share by population.

Sel	State	Observed Tickets	Expected Tickets	Obs/Exp	Population	Pop Share	Chi Component
Run analytics to populate state representation table.

Overlays

Enable overlays (display-only)

Overlays annotate records and analytics with time-aligned context bands/markers. They do not modify dataset logic or predictive scoring.

Categories

Macro Policy Ops Market

Import Overlays

Status: ready.

Overlay Coverage (descriptive)

Overlay	Category	Type	Records Covered	Outliers Covered
Enable overlays and run analytics to populate coverage.

Cluster Overlay Coverage (descriptive)

Cluster	Records	Top Overlays (by record coverage)	Outliers in Cluster
Enable overlays and run analytics to populate cluster coverage.

Coverage percentages are computed against the currently loaded dataset and current outlier sensitivity.

Official Validation Mode (manual paste)

Paste official draw detail text to cross-check jackpot/cash/winner states. This creates a local validation log.

Date	Status	Mismatches
No validations yet.

Analysis Explanations

Enable explainability (traceable outputs)

Selections are pulled from the Pattern Analytics tables (Cluster / Outlier / State Share). This module is display-only and does not affect predictions, scoring, or dataset logic.

Current Selection

State: —

Cluster: —

Outlier Date: —

Outlier z: —

Explainability Output

Select a row and run an explanation action.

Scoring

Status: Not Run

Risk Mode

Candidate Input (optional)

Deliverable #1 expects candidate generation to be delegated to your existing engine. For this scaffold, you may paste candidate rows in the format: 01-12-27-41-63|PB-19 (one per line). If empty, vE9 will attempt to read candidates from the “Previous Predictions” table if available.

Candidates: 0

Run Details

Run ID: —

Fingerprint: —

Enabled Modules: —

(precheck log)

Jackpot Event Forecast (Experimental)

Status: Not Run

Produces a deterministic Top 10 list of observational event forecasts (When / Where / How / Why). This panel never states win odds and never uses causal narratives.

No output yet.

D6 Self-Tests (Fail-Closed + Determinism)

These tests verify: (1) fail-closed behavior when required inputs are missing, (2) deterministic repeatability given identical inputs, and (3) map highlight gating. They do not modify upstream vE9 logic and can be cleared at any time.

US Winners Map

Visible

Display-only interactive USA map for state-level winner distribution . Hover to preview; click a state to view structured details.

Winner Share Delta vs Expected CXT Overlay (i)

State:

Note: Amount details will display only if present in the local offline dataset for this build.

Enhancement #9 — Explainability Continuity (D5)

Visible

vE9 does not generate new explainability narratives. It binds to, references, and reuses the locked vE3.5.10 explainability artifacts and augments them only with transparent score math and bounded CXT disclosure (non-causal).

Explainability Reference Model (EXREF)

Canonical pointer format: EXREF::<artifactType>::<artifactId>::<sectionId>

Examples:

EXREF::CLUSTER::DOMINANT_MONTHS::SUMMARY
EXREF::OUTLIER::RARE_CONDITIONS::DETAILS
EXREF::STATE::REPRESENTATION::OVER_UNDER_SUMMARY
EXREF::OVERLAY::MACRO_POLICY::NARRATIVE

Validity Checklist (Fail-Closed)

A vE9 prediction is invalid unless all are satisfied:

At least one EXREF exists for each enabled scoring module
No EXREF references a disabled module
CXT disclosure is present only if CXT applied
EXREFs map to actual locked vE3.5.10 explainability sections
Audit ledger stores the EXREF list verbatim

If any check fails, the prediction is excluded from the output panel.

Decomposition path: Prediction (UI) → auditRef → Audit Ledger Entry → Module Scores → Explainability Refs (EXREF) → Locked vE3.5.10 sections.

“Why?” drill-down resolves strictly via the audit ledger and surfaces EXREF pointers and locked explainability references without rewording any vE3.5.10 text.

Enhancement #9 — Ethical & Statistical Framing (D6)

Visible

This section documents what vE9 outputs mean, what they do not claim, and how to interpret them responsibly. It is informational only and does not alter the scoring contract or locked explainability narratives.

What vE9 does

Ranks candidate sets deterministically using the locked scoring contract (Deliverable #3) and the audit ledger.
Assigns a confidence tier using percentile banding only (A/B/C/D), relative to the candidate set for that run.
Provides a short rationale using fixed phrase mappings based on the largest absolute module contributions (drivers).
Passes through locked explainability references (vE3.5.10) unchanged, enabling “Why?” drill-down via the audit ledger.

What vE9 does not claim

No claim of improved lottery odds, win guarantees, or causal inference about outcomes.
No claim that tiers represent absolute probabilities; tiers are relative ranks within the evaluated candidate pool.
No claim that contextual indicators (CXT) cause or predict draws; CXT is disclosed as non-causal and isolated.
No claim of state-specific, political, or demographic targeting; the model does not encode such causal assertions.

How to interpret the output

Use tiers as an ordering heuristic, not as a forecast of real-world outcomes.
If scores are tightly clustered, treat differences between nearby ranks as practically insignificant.
Only trust “Why?” explanations that resolve to the audit ledger and locked vE3.5.10 sections.
When required ledger paths are missing or validation fails, vE9 fails closed and excludes outputs.

Disclaimer: vE9 confidence tiers are relative ranking bands derived from candidate percentiles for a single run, not absolute winning probabilities.

Implementation note: This panel is informational. It must not mutate scoring inputs, module weights, explainability text, or historical data state.

Enhancement #9 — Governance Pack (D7)

Visible

Versioned governance constants and schema guards. This section is informational and enforces fail-closed validation for vE9 run artifacts.

PB_E9_CONFIG_V1 (runtime view)

(click “Show vE9 Config”)

Texas Powerball Generator

Historical Data

Methodology Rationale

Drawing History

Top Duplicates

Generate Powerball Play Slip

Exports

Analysis

US Winners Map

Enhancement #9 — Explainability Continuity (D5)

Enhancement #9 — Ethical & Statistical Framing (D6)

Enhancement #9 — Governance Pack (D7)

Enter Winning Numbers