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.