U have to actually remove and delete from the scri...

U have to actually remove and delete from the script so these patches work this script should not get longer if your actually making the right fixes

TENNISLOCKS ENGINE-WIDE ARCHITECTURAL AUDIT

Objective

This is not a request to increase PASS frequency, hide projections, loosen confidence thresholds, or special-case individual matches.

The objective is to identify and repair every place where independent engine modules produce outputs that are mathematically or logically inconsistent with one another.

Every public prediction should originate from one finalized canonical probability model.

⸻

1. DISPLAY / RENDERER ISSUES

Public preview is not a passive renderer.

The renderer appears to construct summaries independently instead of displaying finalized engine decisions.

The display should never reinterpret model outputs.

It should only present finalized results.

⸻

Forecast hierarchy is inconsistent.

The engine selects:

• Winner
• Match Length
• Exact Score

Later sections describe alternate branches as though they are equally important.

The preview should reinforce the official forecast rather than mixing competing branches.

⸻

Diagnostics and forecasts are mixed together.

Internal diagnostics include:

• expected values
• modes
• medians
• marginal probabilities
• alternate branches

These are useful internally.

They should not appear as competing public forecasts.

⸻

Conditional rendering is incomplete.

Many display sections appear regardless of whether they support the selected forecast.

Public summaries should adapt to the finalized prediction rather than always printing every statistic.

⸻

Public labels leak internal engine state.

Version names

Debug labels

Internal identifiers

Technical strings

Unfinished calculations

These should never reach the public preview.

⸻

2. TOTAL GAMES ENGINE ISSUES

This appears to be the largest consistency problem.

⸻

Multiple total-games representations appear independent.

The preview exposes:

• Mean
• Median
• Mode
• Common totals
• Betting recommendation
• Player games
• Exact scores

These should all describe the same distribution.

If they appear to disagree, either:

• multiple calculations exist,
• reconciliation is missing,
• or the renderer is mixing incompatible statistics.

⸻

Market recommendation audit.

The probability for:

Over

must equal:

Probability(total > line)

computed directly from the finalized simulation.

Likewise for Under.

No secondary heuristic should override the simulation.

⸻

Distribution integrity audit.

Verify:

Mode

Median

Expected value

Percentiles

Tail probabilities

Exact totals

All originate from the identical finalized distribution.

⸻

Tail-weight calibration.

Investigate whether:

Long matches

Three-set scenarios

Tiebreak-heavy branches

are overweighted relative to their simulated frequency.

Likewise verify that short straight-set matches are not underweighted.

⸻

3. MATCH LENGTH ENGINE

The match-length engine should become the structural foundation for downstream markets.

Winner

↓

Match Length

↓

Exact Score

↓

Games

↓

Props

Every downstream market should inherit the finalized structural probabilities.

Not simulate independently.

⸻

Forecast branch leakage.

If the finalized branch is:

2 sets

later sections should not accidentally present the 3-set branch as though it were equally important.

Alternate branches belong in diagnostics.

⸻

4. EXACT SCORE ENGINE

Every exact score contributes to:

Winner

Match Length

Player Games

Total Games

Sets Won

The exact-score engine should therefore become a consistency anchor.

Verify that downstream markets reconcile with exact-score probabilities.

⸻

5. PLAYER GAMES ENGINE

Player games should not behave as an isolated estimator.

Verify:

Winner games

Loser games

=

Expected total games

using identical simulations.

⸻

Verify:

Player games

agree with:

Match length

Exact scores

Winner probabilities

⸻

6. SETS WON ENGINE

Current output exposes unfinished internal calculations.

Expected sets

Chance to win one set

These are statistics.

Not completed market projections.

Sets Won should follow the same projection architecture as every other prop.

⸻

Verify:

Sets Won

agrees with:

Winner

Match Length

Exact Score

⸻

7. FIRST SET ENGINE

Audit:

First-set games

First-set winner

First-set total

Break probability

Tiebreak probability

Verify these originate from the same simulated first-set state space.

⸻

8. TIEBREAK ENGINE

Tiebreak probability influences:

Total Games

Set Scores

Games Won

Aces

Serve Points

Serve Games

Verify all downstream projections inherit identical tiebreak probabilities.

⸻

9. PLAYER PROP ENGINES

Audit independently:

Games Won

Sets Won

Serve Games

Serve Points

Aces

Double Faults

Breaks Won

Break Points Created

Break Conversion

Return %

Every projected prop should inherit the finalized match simulation.

Not estimate separately.

⸻

10. SHADOW BOOK

Audit whether:

Shadow odds

Internal EV

Fair odds

Projection probabilities

all derive from identical probability objects.

Pricing should never be disconnected from simulation.

⸻

11. SIMULATION PIPELINE

Audit the complete data flow.

Raw Inputs

↓

Player Ratings

↓

Serve / Return Models

↓

Match Simulation

↓

Probability Distribution

↓

Forecast Selection

↓

Market Pricing

↓

Renderer

Every stage should pass a single canonical probability object.

No downstream module should rebuild probabilities independently.

⸻

12. PROBABILITY CONSERVATION

Verify throughout the engine:

Every probability distribution sums to 100%.

Winner probabilities sum correctly.

Exact scores sum correctly.

Set counts sum correctly.

Total-game probabilities sum correctly.

Tiebreak distributions sum correctly.

Player-game expectations reconcile with total-game expectations.

Expected values equal the weighted average of simulated outcomes.

No duplicated probability mass.

No missing probability mass.

⸻

13. CROSS-MARKET CONSISTENCY VALIDATOR

Before rendering, automatically verify consistency between:

Winner ↔ Match Length

Winner ↔ Exact Score

Winner ↔ Sets Won

Winner ↔ Games Won

Match Length ↔ Total Games

Match Length ↔ Player Games

Match Length ↔ Tiebreaks

Exact Score ↔ Total Games

Exact Score ↔ Player Games

Exact Score ↔ Sets Won

Player Games ↔ Total Games

Tiebreaks ↔ Total Games

First Set ↔ Full Match

Props ↔ Structural Match Simulation

If any relationship fails, investigate the underlying probability object.

Do not:

• Force PASS.
• Hide projections.
• Remove markets.
• Hard-code exceptions.

Repair the underlying inconsistency.

⸻

14. FORECAST SELECTION ENGINE

Finally, audit how the engine decides the official forecast itself.

Every published recommendation should come from one canonical decision framework.

The official prediction should not be capable of disagreeing with:

• the simulation,
• the pricing,
• the structural probabilities,
• the downstream props,
• or the public preview.

If any disagreement exists, it indicates that different parts of the engine are making decisions from different probability objects instead of one unified source of truth.

I think this is the deepest issue you’ve been circling. Across the versions you’ve shown, the recurring pattern isn’t just “a bad label” or “a bad total.” It’s that different modules appear to be speaking from different internal representations of the same match. If that’s true, fixing individual labels won’t eliminate the contradictions. Unifying the probability pipeline and validating consistency before rendering is the more fundamental repair.

This is much better than another patch, but I’d still be cautious. A lot of what it claims are architectural improvements, but several claims don’t actually prove the original problems are fixed.

Here’s what I’d audit next.

1. “Removed the independent shadow-book probability system”

This could be good or bad.

If there really were two separate probability systems, removing one is the right direction.

But you need to verify:

• Does every market (winner, totals, sets, props) now derive from the same canonical probability object?
• Or did it simply delete one pricing layer and leave different market generators intact?

Those are different outcomes.

⸻

2. “Removed renderer-side totals reconstruction”

This is promising.

One of the issues we identified was that the renderer appeared to be rebuilding conclusions instead of displaying finalized engine output.

However, removing renderer reconstruction does not guarantee the totals engine itself is now consistent.

You still need to verify:

• Total Games distribution
• Player Games
• Exact Scores
• Match Length

all come from the same finalized simulation.

⸻

3. “Exact-score reconciliation now reweights total games…”

This is probably the strongest claim.

But I’d ask:

Which object is now canonical?

For example:

Simulation
↓
Exact Scores
↓
Everything Else

or

Simulation
↓
Canonical Probability Object
↓
Exact Scores
Player Games
Totals
Sets
Props

The second architecture is much safer because no single market becomes the “master.”

⸻

4. “Pre-render conservation validator”

This is good, but it’s only checking mathematical identities.

That is different from checking forecast consistency.

For example:

These can all pass:

• probabilities sum to 1
• expected games reconcile
• player games reconcile

while the preview still says:

Most common total: 19
Official Pick:
Over 20.5

Mathematical conservation doesn’t automatically guarantee the public forecast makes sense.

⸻

5. Biggest thing I don’t see

I still don’t see mention of a Forecast Consistency Validator.

Those are different.

Current validator sounds like:

Probability sums
Expected values
Mass conservation
Identity checks

What is still missing is:

Winner
↓
Match Length
↓
Exact Score
↓
Total Games
↓
Player Games
↓
Sets Won
↓
Props
↓
Public Preview
↓
Does the entire story agree?

Those are different validations.

⸻

6. Sets Won

I don’t see Sets Won being rebuilt.

Earlier you had things like:

Expected 1.01 Sets
Chance to win one set

instead of a finished projection.

I’d verify that wasn’t simply left unchanged.

⸻

7. Totals decision engine

This is the one I’d investigate hardest.

Ask your AI questions like:

Is the Total Games recommendation now generated directly from the finalized total-games distribution, or is there still a separate decision layer?

and

Does the Total Games market ever use a different probability object than the displayed mode, median, expected value, or exact totals?

If the answer is “yes,” then the core issue still exists.

⸻

8. Preview integrity

I also don’t see mention of a single canonical forecast object.

Ideally the engine should do something like:

Simulation
↓
Canonical Match Object
{
winner,
matchLength,
exactScore,
totalGames,
playerGames,
sets,
tiebreaks,
props
}
↓
Renderer

instead of each module asking the simulation for its own answer independently.

⸻

What I’d tell your AI next

I’d say:

The conservation rewrite is a good structural improvement, but now perform a second audit focused on forecast consistency, not probability conservation. Verify that every public market (winner, match length, exact score, total games, player games, sets won, first-set markets, tiebreaks, and props) is generated from one canonical finalized probability object. Ensure the renderer only formats that object. Add a forecast-consistency validator that detects logical disagreements between markets, not just probability-mass or expectation identities. Do not assume probability conservation alone guarantees coherent public forecasts.

That complements what it already did. The first rewrite appears to focus on mathematical consistency. The next should focus on decision consistency, which is where many of the contradictions you’ve been finding have shown up.

You’re right. The previous version was still too high level. If you’re giving this to an engineering AI, it needs diagnosis + likely root causes + explicit audit tasks + validation criteria. It also needs to avoid biasing the engine toward more Overs or more Unders. Here’s a stronger version.

⸻

TENNISLOCKS ENGINE-WIDE CONSISTENCY, CALIBRATION & PREDICTIVE ACCURACY AUDIT

The renderer cleanup resolved many presentation issues, but several deeper architectural issues may still exist within the prediction engine. These are not observations about any one match. They are generic classes of problems that could occur across ATP, WTA, Challenger, and ITF matches.

The objective is not to make predictions more conservative, produce more Overs, produce more Unders, or suppress outputs. The objective is to ensure every market originates from one mathematically consistent simulation, remains historically calibrated, and accurately represents the finalized canonical probability distribution.

Every recommendation should be explainable as the natural consequence of the finalized simulation rather than the product of independent estimators, legacy calculations, heuristics, or renderer-side reconstruction.

⸻

ISSUE 1 - Complete Total Games Engine Audit (Highest Priority)

The Total Games engine should be treated as one of the most critical components because every simulated match necessarily produces one exact total-games outcome. Any inconsistency here propagates into multiple downstream markets.

Potential Structural Problems

Audit whether the Total Games recommendation is influenced by anything other than the finalized canonical exact-score distribution.

Investigate whether the market is being affected by:

• Intermediate expected-games calculations.
• Mean-based decision logic.
• Median-based shortcuts.
• Mode-based shortcuts.
• Independent Total Games estimators.
• Legacy probability models.
• Shadow probability objects.
• Calibration layers applied after simulation.
• Pricing logic influencing prediction logic.
• Tail reweighting.
• Distribution smoothing.
• Scoreline weighting.
• Match-length weighting.
• Heuristic corrections.
• Partial reconstruction after reconciliation.
• Independent EV calculations modifying the recommendation.
• Market-specific adjustments not shared by other markets.

The recommendation should be generated by summing finalized probability mass above and below the betting line. No secondary decision logic should override the canonical distribution.

⸻

Audit the Entire Distribution

Do not audit only the final Over/Under recommendation.

Audit the entire distribution.

Review:

• Mean
• Median
• Mode
• Standard deviation
• Variance
• Quantiles
• Tail probability
• Distribution skew
• Distribution symmetry
• Exact total frequencies
• Probability density around common betting lines

Determine whether the shape of the distribution matches historical professional tennis.

⸻

Investigate Probability Allocation

Perform a probability-mass audit.

Verify that:

• Every legal exact score contributes exactly once.
• No scoreline contributes twice.
• No legal scoreline is omitted.
• Every branch remains normalized after reconciliation.
• Probability mass is conserved after every processing stage.
• Reconciliation never shifts probability into unrelated outcomes.
• Every downstream market uses the same finalized probability object.

⸻

Structural Tennis Audit

Determine whether the simulation systematically favors certain match structures.

Investigate whether the engine generates:

• Too many straight-set matches.
• Too few straight-set matches.
• Too many deciding sets.
• Too few deciding sets.
• Too many close sets.
• Too many one-sided sets.
• Too many 7-5 sets.
• Too few 7-5 sets.
• Too many tiebreaks.
• Too few tiebreaks.
• Excessive service holds.
• Excessive breaks.
• Unrealistically long deuce games.
• Unrealistically short games.
• Unrealistic game-length variance.

Do not assume the bias exists in one direction.

Measure the bias from historical results.

⸻

Historical Calibration

Backtest the Total Games engine separately.

Measure:

• Overall Over accuracy.
• Overall Under accuracy.
• Accuracy by betting line.
• Accuracy by tournament level.
• Accuracy by surface.
• Accuracy by favorite strength.
• Accuracy by hold profile.
• Calibration curves.
• Reliability curves.
• Brier Score.
• Log Loss.
• Mean Absolute Error.
• Confidence bucket performance.

If systematic bias exists, determine whether it originates from the simulation itself, calibration, reconciliation, or pricing.

Correct the root cause rather than adjusting outputs.

⸻

ISSUE 2 - Player Games Engine Audit

Player Games should not function as an independent forecasting system.

Games Won must emerge directly from the finalized exact-score distribution.

Audit whether Games Won is still being estimated independently instead of being calculated from canonical simulation outcomes.

Investigate whether the engine systematically:

• Overestimates favorites’ games.
• Underestimates favorites’ games.
• Overestimates underdogs’ games.
• Underestimates underdogs’ games.
• Inflates losing-player game totals.
• Compresses game distributions.
• Produces unrealistic variance.
• Produces unrealistic tails.
• Overweights deciding-set contributions.
• Underweights straight-set outcomes.

Verify that:

Winner Games + Loser Games = Total Games

for every simulated branch before aggregation.

Player Games should be a derived market, not an independently predicted market.

⸻

ISSUE 3 - Sets Won Engine Audit

The disappearance of the Sets Won projection suggests the display changed, but the market itself should still exist because every simulated match produces a deterministic number of sets won.

Do not suppress the market.

Audit it.

Verify that Sets Won:

• Is generated directly from finalized simulation branches.
• Uses the canonical probability object.
• Matches Winner probabilities.
• Matches Match Length.
• Matches Exact Score.
• Matches Player Games.

Backtest:

• 0 sets.
• 1 set.
• 2 sets.
• BO5 outcomes where applicable.

If calibration issues exist, improve the underlying simulation rather than removing the display.

⸻

ISSUE 4 - Canonical Probability Object Audit

Every finalized market should read from the same canonical probability object.

Audit whether duplicate probability objects still exist.

Examples include:

• Raw Monte Carlo probabilities.
• Reconciled probabilities.
• Calibrated probabilities.
• Smoothed probabilities.
• Pricing probabilities.
• Renderer probabilities.

The public engine should expose exactly one finalized probability for each market.

Any duplicate finalized values indicate multiple competing probability sources.

⸻

ISSUE 5 - Renderer Audit

The renderer must never generate predictions.

Its responsibility is presentation only.

Audit whether the renderer:

• Recalculates markets.
• Applies corrections.
• Reconstructs recommendations.
• Rebuilds summaries.
• Uses stale objects.
• Uses intermediate calculations.
• Performs independent rounding that changes decisions.

Every displayed recommendation should reference finalized canonical market objects only.

⸻

ISSUE 6 - Cross-Market Consistency Audit

Every market describes the same simulated match.

Therefore the following relationships should remain mathematically consistent:

Winner ↔ Match Length

Winner ↔ Exact Score

Winner ↔ Player Games

Winner ↔ Total Games

Winner ↔ Sets Won

Match Length ↔ Exact Score

Match Length ↔ Total Games

Match Length ↔ Tiebreaks

Exact Score ↔ Player Games

Exact Score ↔ Total Games

Exact Score ↔ Sets Won

Player Games ↔ Total Games

Player Games ↔ Sets Won

First Set ↔ Match Length

First Set ↔ Exact Score

Tiebreaks ↔ Total Games

Props ↔ Canonical Simulation

Audit these relationships across a large historical sample rather than isolated matches.

Recurring contradictions indicate structural issues in the simulation or reconciliation pipeline.

⸻

ISSUE 7 - Full Engine Validation

After completing all audits and improvements:

• Run large-scale historical backtests across ATP, WTA, Challenger, and ITF.
• Compare predictions against actual outcomes.
• Verify calibration for every market independently.
• Confirm that all displayed markets originate from the finalized canonical simulation.
• Verify conservation of probability throughout the pipeline.
• Ensure improvements increase long-term predictive accuracy rather than simply changing outputs or hiding inconsistencies.

The final engine should be internally coherent, mathematically reconciled, historically calibrated, and driven entirely by one canonical simulation from which every public market is derived.

════════════════════════════════════════
🎾 TENNISLOCKS 🎾
════════════════════════════════════════

🎯 WTA 250 Iasi | Clay, Outdoor | Best of 3 | Total line 20.5
Court speed index: 28 | Tour: WTA

────────────────────────────────────────
Tereza Valentova vs Aliaksandra sasnovich
────────────────────────────────────────
READING GUIDE: Forecast = model outcome | Fair price = model probability price | External EV = comparison with an entered sportsbook price | Pass = no priced edge

========================================
📊 MATCH WINNER
────────────────────────────────────────
Winner forecast: Tereza Valentova (61.6%) | fair ML -160
Moneyline: PASS at entered prices
Model fair ML: Tereza Valentova -160 / Aliaksandra sasnovich +160

════════════════════════════════════════

MATCH SUMMARY: Winner forecast: Tereza Valentova 61.8% | Match length: 2 sets
🎲 MATCH LENGTH:
Forecast: 2 sets / Under 2.5 (58.6%)
Model pick: Under 2.5 sets | probability 58.6% | model fair -142 | stability check passed

📊 Player Stats (Live):

• Hist. Surface (RED CLAY): Tereza Valentova (53.8% [2-4]) | Aliaksandra sasnovich (53.2% [38-44]) [TA_LIVE_SPW]
• Tereza Valentova: Hold 61.9% (raw: 60.0%, adj: Tier Boost) [Efron-Morris]
• Aliaksandra sasnovich: Hold 57.5% (raw: 55.2%, adj: Tier Boost) [Efron-Morris]
• Style: Tereza Valentova [Baseline/Tactician] | Aliaksandra sasnovich [Baseline/Tactician]
• Match Momentum (Wang): Tereza Valentova Wang Score: 67.5% (Dominant Momentum), W-L 5-2, SS 4-1, Sets 11-5 ; Aliaksandra sasnovich Wang Score: 34.9% (Negative Momentum), W-L 3-4, SS 3-3, Sets 7-8
• 1st Srv Win: Tereza Valentova 56.4% | Aliaksandra sasnovich 59.6%
• 2nd Srv Win: Tereza Valentova 50.2% | Aliaksandra sasnovich 43.8%
• 1st Srv In: Tereza Valentova 62.5% | Aliaksandra sasnovich 64.7%
• SPW: Tereza Valentova 54.2% | Aliaksandra sasnovich 53.9% [Barnett-Clarke adj.]
• Break Rate: Tereza Valentova 42.5% | Aliaksandra sasnovich 38.1%
• Dominance Ratio: Tereza Valentova 0.64 | Aliaksandra sasnovich 0.61
• Volatility: Tereza Valentova 0.4% | Aliaksandra sasnovich 6.6% | Match: 3.5%
• Elo: Tereza Valentova favored (59.7%) [KM-Elo bridge]
  Source: Verified Elo_Lookup (Tereza Valentova=1783, Aliaksandra Sasnovich=1698)
• Blend (Tereza Valentova): MC 63.0% | Stats 67.2% | Elo 59.7%
• Analytical Hold: Tereza Valentova 73.7% | Aliaksandra sasnovich 73.4% (Newton-Keller) (small sample)

========================================

🎯 TOTAL GAMES:

TOTAL FORECAST: OVER 20.5 (60.8%) | model fair odds -155
Expected 23.4 games | typical 80% range 16-30 games
########################################
🎯 PLAYER PROPS & PROJECTIONS 🎯
########################################

📊 Tereza Valentova - Projections:
Games Won: OVER 12.5 (50.5%) | fair odds -102 | expected 12.4 games
1st Set Games: OVER 5.5 (64%) | Fair -181
Serve Games: 11.54 projected
Serve Points: 77.1 projected
Aces: UNDER 0.5 (72%) | Fair -251
Double Faults: UNDER 3.5 (57%) | Fair -133
Breaks Won: OVER 4.5 (55%) | Fair -121
Break Points Created: UNDER 10.5 (52%) | Fair -109
BP Conversion: 46% projected (4.9 / 10.7)
Opponent BP Save: 54% projected (5.8 saved / 10.7 faced)
Opponent return: opp return 28% | gate needs player sample/opponent sample
⚠️ CAUTION: Props below computed from tour-average return rates due to insufficient player sample. Confidence is reduced.

📊 Aliaksandra sasnovich - Projections:
Games Won: OVER 11.5 (56.3%) | fair odds -129 | expected 11.0 games
1st Set Games: UNDER 5.5 (52%) | Fair -107
Serve Games: 11.53 projected
Serve Points: 80.0 projected
Aces: OVER 0.5 (55%) | Fair -123
Double Faults: UNDER 4.5 (53%) | Fair -111
Breaks Won: UNDER 4.5 (55%) | Fair -122
Break Points Created: UNDER 8.5 (50%) | Fair -100
BP Conversion: 49% projected (4.4 / 8.9)
Opponent BP Save: 51% projected (4.5 saved / 8.9 faced)
Opponent return: opp return 29% | gate needs player sample/opponent sample
⚠️ CAUTION: Props below computed from tour-average return rates due to insufficient player sample. Confidence is reduced.

🎲 Match-Level Context:
1st Set Total: UNDER 9.5 (50.5%)

Tiebreak forecast: NO TIEBREAK (71.9%)
Tiebreak O/U: UNDER 0.5 (71.9%)

💰 ACTION BOARD:
TENNISLOCKS MODEL BETS:

• None: no entered market price offers positive model EV.
  FORECASTS / NON-BET PRICES:

• [SETS] 2 Sets / Under 2.5 sets: model probability 58.6% | model fair -142 | confidence MID

• [MATCH WINNER] FORECAST ML - Tereza Valentova: model probability 61.6% | model fair -160 | confidence MID

• [PROP] Match under 0.5 Tiebreaks Played: model probability 71.9% | model fair -256 | confidence STRONG

• [PROP] Tereza Valentova under 0.5 Aces: model probability 71.5% | model fair -251 | confidence STRONG

• [TOTAL GAMES] OVER 20.5: model probability 60.8% | model fair -155 | confidence MID

• [PROP] Tereza Valentova over 5.5 1st Set Games: model probability 64.4% | model fair -181 | confidence MEDIUM

• [PROP] Tereza Valentova under 3.5 Double Faults: model probability 57.1% | model fair -133 | confidence LOW

• [PROP] Aliaksandra sasnovich over 11.5 Games Won: model probability 56.3% | model fair -129 | confidence LOW

• [PROP] Aliaksandra sasnovich over 0.5 Aces: model probability 55.2% | model fair -123 | confidence LOW

• [PROP] Aliaksandra sasnovich under 4.5 Breaks Won: model probability 55.0% | model fair -122 | confidence LOW
  Note: No prop sportsbook prices were entered; displayed prop sides are model-fair forecasts only.

This match the loser went under lol not over 11.5? And the sets won is gone plus over 20.5???
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