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Scoring Audit

Model v8.0Updated Oct 11, 2026

Every week, we score every winning Powerball draw against our criteria and use AI to check that each pass rate sits where combinatorics predicts. Weights never adjust themselves — every change is a versioned revision. This page shows the results — no black boxes.

In 97% of the 1,413 draws since October 2015, the winning combination failed at least one of our nine filters.

A fair lottery predicts 98% (observed: 42 perfect-scoring winners out of 1,413). That is the point of this page: we describe the structure of combinations, we do not predict outcomes, and every combination has identical odds.

Draws Analyzed
1,412
Avg Score (All-Time)
63.9 / 100
Avg Score (Recent 50)
62.1 / 100
Score Drift
-1.8

Latest audit: Oct 11, 2026 · Recent window: Jun 15, 2026 – Oct 7, 2026

Criteria Performance

CriterionMax PtsAll-Time PassRecent PassDriftConfidenceAvg Pts (All)Avg Pts (Recent)
Parity1063%58%-552%6.35.8
High/Low1066%62%-443%6.66.2
Sum Range967.5%72%+4.548%77.1
Spread1064.2%76%+11.890.5%7.58.2
Unique Digits1933.4%30%-3.437%10.410.7
Modular Balance1062.7%70%+7.370%6.37
Tens Diversity968.6%64%-4.648%6.25.8
Even Spacing1453.1%40%-13.193.6%7.45.6
Primes969.5%64%-5.556%6.35.8
Stable (<5)Watch (5-10)Flagged (>10)

What This Audit Proves

Consistency

The system behaves the same way every draw. Criteria pass rates stay within expected ranges across thousands of draws.

Structure

Picks are not random. They follow defined combinatorial criteria that align with how real draws are structured.

Transparency

No cherry-picking. Every criterion, every weight, every drift metric is published here. You see exactly what we see.

Every lottery combination has the same odds. What changes is how combinations are selected. This audit shows that Balliqa consistently selects from a structured subset — rather than picking randomly.

AI Analysis

high confidenceLast run: Oct 11, 2026

Overall rates are consistent with a fair lottery. All criteria except two fall within expected ±1.5σ variation. Recent avg score 62.1 vs all-time 63.9 (drift -1.8) is normal noise over 50 draws with ~6.7pt SE.

Flagged Criteria

  • Even Spacing: -13.1pt drift (≈2.0σ) is borderline notable but within 2σ threshold (~±14); monitor next 2-3 weeks for pattern confirmation

Suggestions

  • Track Even Spacing and Spread (which spiked +11.8pt) over next 3 weeks to confirm whether drift persists or reverts to mean
  • No evidence of unfairness; all variation explainable by random sampling in 50-draw windows

Generated by Claude (Anthropic) · AI suggestions are reviewed by humans before any scoring changes are made.

Current Weights

v8.0100 pts
Unique Digits19
Even Spacing14
Parity10
High/Low10
Spread10
Modular Balance10
Sum Range9
Tens Diversity9
Primes9
Drift Rebalance+0-7 bonus

Model Changelog

Split-avoidance release — the score is unchanged; what changed is which qualifying tickets subscribers receive. (1) No shared pool: each subscriber's picks are drawn independently from the full set of qualifying combinations, so subscribers no longer hold copies of one another's tickets (with the old 500-ticket pool, about a third of tickets were duplicated across the subscriber base). (2) Crowd gate: of the 275,193 combinations that pass all nine criteria, only the 187,303 with at most two numbers from 1–31 are eligible. Per-tier winner counts over 1,412 draws show sets with four or five such numbers are held by about 1.5× as many players as average, and sets with at most two by about 7% fewer (0.93×). (3) Powerball tilt: the red ball is drawn in proportion to how under-played it is (index^−6), so the most over-played ball (7, 1.13×) is drawn about a third as often as the least played (1, 0.93×) — a mild lever, since Powerball popularity is nearly flat. All three change only how many people would share a prize, never the odds, which are identical for every combination. The crowd constants are a dated snapshot and change only with a versioned revision.

Analytic bands and fixed weights. Sum Range and Spread now score against bands derived exactly from the 5/69 matrix (sum: mean 175, σ 43.2; spread: mean 46.7, σ 11.9) instead of the historical mean and σ, so a combination's score no longer shifts as draws accumulate and the model has no dependence on past results at all. Retired the weekly audit's automatic weight adjustment: weights are set by combinatorial filter strength in code and change only with a versioned revision — the audit remains a fairness monitor. The selection pool now keeps only combinations that pass every criterion (about 2.4% of all combinations) and de-duplicates them. Weights unchanged from v7.0.

Current-matrix calibration + honest reweighting. All analysis now scoped to the current 5/69 era (draws from 2015-10-07 on); ~591 older draws from the retired 5/59 matrix were being mixed in, biasing every frequency, σ-band, and drift figure. Verified against a 5M-combination Monte-Carlo: on current-era data all criteria sit within their 95% combinatorial bands (the lottery is fair). Recomputed every weight from exact combinatorial filter strength — weight ∝ (1 − pass rate), normalized to 100 by largest-remainder rounding; the prior v6.0 weights deviated from this formula by up to 7 pts (Even Spacing was the 2nd-hardest filter yet weighted lowest). Dropped Range Coverage as redundant (φ up to 0.41 with Sum Range, 0.33 with Tens Diversity) — down to 9 criteria.

Purely combinatorial model. Replace Consecutive Pair (empirical, 26.6% pass rate) with Modular Balance (all 3 residues mod 3, 64.3% pass rate). Remove Drift Rebalance overlay (empirical mean-reversion assumption). Reweight all criteria by filter strength — weight proportional to (1 - pass rate), normalized to 100. Unique Digits (35.1% pass rate) now highest at 17 pts. Protected criteria updated to Parity, High/Low, Unique Digits. All 10 criteria now derivable from pure combinatorial math with zero reliance on historical patterns.

Add Drift Rebalance overlay (+0-7 bonus pts). Compares last 20 draws against full-history baselines across 8 structural metrics (parity, high/low, sum, digit sum, primes, gap entropy, consecutive pairs, decades). Picks that lean against metrics drifted beyond 1σ earn up to 1 pt each, capped at 7. Operates at the distribution level only — no individual number targeting. Base 100-pt model unchanged.

Remove gambler's fallacy criteria. Replace Co-occurrence (5 pts, worse than random prospectively at 36% vs 50%), Hot/Cold Mix (5 pts, 35.6% vs 50%), and PB Weighting (5 pts, 70.1% vs 75%) with three combinatorially-grounded criteria: Range Coverage (6 pts, all 3 thirds represented, 62%), Tens Diversity (5 pts, 4+ tens groups, 66%), and Even Spacing (4 pts, all gaps ≤ 2x ideal, 59%). All criteria now verifiable from pure combinatorial math with zero reliance on historical patterns persisting.

Major model overhaul. Remove Decade Spread (97% pass rate, no discrimination) and Drought Bonus (gambler's fallacy, −20 drift). Add Consecutive Pair (6 pts, 28% of draws have one) and PB Weighting (5 pts, PB drought above 25th percentile). Graduate Sum Range (18/9/0 at 1σ/1.5σ), Spread (13/6/0), and Unique Digits (7/3/0 at 5/4 unique). Loosen Hot/Cold Mix to include 2+ hot or 2+ cold. Reduce Primes 8→5 pts, bump Co-occurrence 4→5 pts.

Replace Anti-Birthday (4 pts) with Co-occurrence (4 pts) — scores picks containing historically co-occurring number pairs. Recalibrate Drought Bonus thresholds (35→20 floor, 80→60 divisor) to fix −21.4 drift.

Redistribute points to maintain 100-point scale. Parity/High-Low/Sum Range 15→18 pts each, Spread 10→13 pts. Max stays at 100.

Drought Bonus 10→5 pts, Decade Spread 8→4 pts, Anti-Birthday 7→4 pts. First AI audit-driven recalibration.

Initial scoring model with 10 criteria, max 100 pts.

Reading the Audit

All-Time Avg

How the average winning draw scores against our criteria. Each criterion's all-time pass rate should closely match its combinatorial pass rate — confirming real draws behave like random samples from C(69,5).

Recent Avg

The average score across the last 50 draws. Comparing this to the all-time average reveals whether the model's criteria are holding steady or drifting — the difference between these two is the drift value.

Drift

The difference between recent (last 50 draws) and all-time pass rates. A 50-draw window has a standard error of about ±7 points, so drift inside ±10 is ordinary sampling noise; beyond ±14 (2σ) it is flagged for review.

AI Confidence

How confident the AI analyst is in its assessment. Based on sample size and drift significance. High = clear signals, medium = some uncertainty, low = insufficient data.

Adjusted

Weights are fixed in code by combinatorial filter strength and change only with a versioned model revision (see the changelog below) — never by the audit. Before v7.1 the audit could nudge weights; those historical adjustments are still shown for transparency.

Audit History

DateModelDrawsAll-Time AvgRecent AvgDriftAI ConfidenceAdjusted
Oct 11, 2026v7.11,41263.9 / 10062.1 / 100-1.8high—
Oct 4, 2026v7.11,40963.9 / 10062.7 / 100-1.2high—
Sep 27, 2026v7.11,40663.9 / 10062.9 / 100-1high—
Sep 20, 2026v7.11,40364 / 10064 / 1000high—
Sep 13, 2026v7.11,40064 / 10063.8 / 100-0.2high—
Sep 6, 2026v7.11,39764 / 10064.3 / 100+0.3high—
Aug 30, 2026v7.01,39463.9 / 10063.1 / 100-0.8medium—
Aug 23, 2026v7.01,39163.9 / 10063.2 / 100-0.7medium—
Aug 16, 2026v7.01,38863.9 / 10064.6 / 100+0.7high—
Aug 9, 2026v7.01,38563.9 / 10065.5 / 100+1.6medium—
Aug 2, 2026v7.01,38263.9 / 10065.4 / 100+1.5medium—
Jul 26, 2026v7.01,37963.9 / 10065.1 / 100+1.2medium—
Jul 19, 2026v7.01,37663.9 / 10066.2 / 100+2.3——
Jul 12, 2026v7.01,37363.9 / 10065.9 / 100+2medium—
Jul 5, 2026v7.01,37063.9 / 10065.3 / 100+1.4high—
Jun 28, 2026v7.01,36863.9 / 10065.9 / 100+2high—
Jun 21, 2026v6.01,95567.4 / 10069.7 / 100+2.3medium—
Jun 14, 2026v6.01,95267.4 / 10069.7 / 100+2.3medium—
Jun 7, 2026v6.01,94967.4 / 10068.2 / 100+0.8medium—
May 31, 2026v6.01,94667.4 / 10068.3 / 100+0.9medium—

How Our AI Audit Works

We use AI to audit ourselves — not to predict numbers. Read the full breakdown of our scoring audit pipeline.

Read the Blog Post

The system is transparent. The math is published. The rest is up to you.

Get your picks before the next draw →

DISCLAIMER: Balliqa is an entertainment product. Every Powerball drawing is an independent random event. Pattern analysis of historical draws does not predict or influence future outcomes. The odds of winning the Powerball jackpot are 1 in 292,201,338.

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