You’re right, the prompt needs to make the AI unde...

You’re right, the prompt needs to make the AI understand that a 3-set match is not automatically an Over and that the engine should not be collapsing the total-games prediction into “2 sets = Under, 3 sets = Over.” That was one of the problems your previous rebuilds were trying to fix.

Use this:

Perform a full audit of the current match-length, set-count, and total-games interaction.

The recent rebuild appears to have increased Under selections, but the main concern is not the number of Unders. The concern is whether the engine has developed a structural bias where it is overvaluing straight-set outcomes and suppressing legitimate 3-set Under scenarios.

A critical modeling rule needs to be enforced:

A 3-set match MUST still be able to produce an Under 22.5, Under 23.5, etc. selection when the simulated score distribution supports it.

The engine cannot operate with the hidden assumption:

2 sets = Under
3 sets = Over

That is incorrect tennis modeling.

Examples of valid 3-set Under paths:

• 6-3, 3-6, 6-2 = 26 games (Over example)
• 6-4, 6-3, 6-4 = 29 games (Over example)
• 6-2, 6-3, 6-1 = 18 games (Under example)
• 6-4, 6-2, 6-3 = 25 games (depends on line)
• 7-5, 6-3, 6-2 = 27 games (depends on line)

The model must evaluate the actual score tree, not simply classify by number of sets.

Current output issue:

Example:
Match forecast:
2 sets / Under 2.5 sets (63.4%)

Totals:
Under 22.5 (53.7%)

Under probability composition:
53.2% from straight-set paths
0.5% from three-set paths

This suggests the engine is barely allowing 3-set paths to contribute to the total-games forecast. Audit whether the total-games PMF is incorrectly inheriting the match-length decision instead of independently calculating from legal score states.

Required fixes:

1. Separate match winner, set count, and total games into independent but connected distributions.

The flow should be:

Player strength/service/return inputs
↓
Set probability model
↓
Match tree simulation
↓
Actual score distribution
↓
Total games distribution

Do not force totals from the match-length forecast.

2. Allow the model to select:

• Under with high 2-set probability
• Under with meaningful 3-set probability
• Over with high 2-set probability
• Over with high 3-set probability

All four outcomes must remain possible.

3. Rebuild the Sets Won display.

Current issue:
The model can show:

Player A:
P(0 sets): 28.7%
P(1 set): 16.4%
P(2 sets): 54.9%

Player B:
P(0 sets): 34.7%
P(1 set): 20.2%
P(2 sets): 45.1%

while simultaneously displaying a 2-set match forecast.

These are marginal probabilities and are confusing because they are not tied to actual match paths.

Replace with conditional match-path probabilities:

Player A 2-0: XX%
Player B 2-0: XX%
Player A 2-1: XX%
Player B 2-1: XX%

Then derive:

• expected sets played
• expected games
• total line probabilities

from those same paths.

4. Audit recent Under increase.

Compare current version against previous calibrated versions.

Measure:

• Under/Over hit rate
• calibration by total line
• 2-set frequency
• 3-set frequency
• average games in 2-set matches
• average games in 3-set matches
• percentage of Unders coming from 3-set matches

If the model is correctly calibrated, 3-set matches should still meaningfully contribute to both Under and Over outcomes.

5. Investigate these possible causes:

• Terminal score calibration shifting too many outcomes downward.
• Hold probability adjustments creating unrealistic short matches.
• Tiebreak probability being suppressed incorrectly.
• PMF reconciliation forcing totals to match the winner model.
• Score root → final PMF shift removing legitimate longer outcomes.
• Three-set branch probability being reduced after winner reconciliation.

Do not tune the engine to produce more Overs or fewer Unders.

The objective is calibration:
When the engine selects Under, it should be because the full simulated score tree supports the edge, including possible 3-set paths.

A correctly functioning tennis total model must allow a close clay match with a high chance of going 3 sets to still land Under when the individual set scores are low enough.

That wording should push it toward fixing the actual architecture instead of just nudging percentages. The key sentence is the one about not inheriting totals from match length. That has been the recurring trap in your engine.

U need to actually fix the script and also my instructions could be completely different from the script so u might have to change a lot more

Yeah, that would be a good idea. The AI working on your engine should know that the previous prompts were symptom-based hypotheses, not confirmed bugs from code inspection. That prevents it from blindly stacking fixes on top of assumptions.

Use this:

Before applying any further fixes, perform an assumption audit.

Important context:

The previous 6+ fix prompts were based on observed output behavior, screenshots, logs, and reported issues. They were NOT based on direct inspection of the full source code. Treat those previous suggestions as hypotheses, not confirmed defects.

Do not assume that every described issue exists exactly as stated.

Required process:

1. Review the actual current engine architecture first.
2. Locate the real functions/modules responsible for:
   • match winner prediction
   • BO3 set simulation
   • match length prediction
   • total games PMF
   • sets won projections
   • tiebreak probability
   • prop projections
   • display formatting
3. For each previous proposed fix, classify it as:
   • Confirmed bug in code
   • Partially valid issue
   • Already solved by existing architecture
   • False positive caused by display/output interpretation
4. Do not add duplicate patches.
   If the current architecture already handles the issue, keep the existing solution and avoid unnecessary complexity.
5. Prioritize root causes over symptom fixes.

For example:
If totals appear biased toward Unders, verify whether the cause is:

• actual simulation probabilities,
• incorrect state transitions,
• reconciliation logic,
• PMF transformation,
• calibration adjustments,
• or only the display layer.

Do not assume:
“More Unders appearing = Under bias.”
It may be a calibration improvement, or it may be a structural issue. Validate with code and simulation.

Before making changes, provide:

• Current architecture explanation
• Confirmed failure points
• Why each change is necessary
• Expected effect on predictions

The goal is not to force previous theories into the engine. The goal is to make the engine mathematically correct based on the actual code.

That prompt basically tells it: “Stop treating my old debugging notes like scripture. Verify them.” Which is exactly what you want after several rebuild cycles, because patches can start turning into a patch jungle where nobody remembers which tree is real anymore. 🌲

There’s a bunch of recent patches that were sssumpyind go back and fix them

Yeah, that context matters. The prompt should not only audit v422/v423. It should force the AI to look at the entire chain of patches from v417 → current and verify that later changes did not accidentally undo earlier fixes.

The important part is: do not tell it those patches were definitely correct. Tell it to classify them. Some may be real architectural fixes, some may have been based on assumptions.

I would send this:

Perform a full historical patch audit from v417 through the current engine version.

Important context:

The previous patches (v417, v418, v419, v420, v421, v422, v423) were created from observed failures, debugging sessions, and output behavior. Some may represent real architectural fixes, while others may have been hypothesis-driven changes.

Do NOT assume every previous patch was correct.

The goal is to inspect the actual source code history and classify every major change as:

1. Confirmed architectural improvement.
2. Valid fix, but implemented incorrectly or incompletely.
3. Valid concern but unnecessary because the architecture already handled it.
4. Display/output issue mistaken for a modeling issue.
5. Regression introduced by a later patch.

Audit each version:

v417 audit

Verify whether winner probability was previously coupled incorrectly with score-shape generation.

Check:

• Does winner probability still influence set-count probability incorrectly?
• Can winner confidence force unrealistic deciding-set probability?
• Is score simulation independent from winner calibration?

v418 audit

Verify terminal-score calibration integration.

Check:

• Was terminal-score calibration previously disconnected from live PMF generation?
• Is calibration now applied at the correct stage?
• Is calibration changing canonical probabilities or only display values?

v419 audit

Verify root-cause decomposition.

Check:

• Was the inflation issue actually present in the legal state-space generation?
• Was reconciliation incorrectly blamed for errors created earlier?
• Does the current state-space model produce realistic score distributions?

v420 audit

Verify calibration placement.

Check:

• Are calibrations applied inside the model where probabilities are generated?
• Are there any remaining display-only corrections?
• Are any calibration tables manually overriding simulation outputs?

v421 audit

Verify red-team cleanup.

Check:

• Which calibrations were removed?
• Why were they removed?
• Were they actually harmful out-of-sample, or only suspected?
• Did removing them improve architecture or only simplify the code?

v422/v423 audit

Verify totals, match length, and Sets Won changes.

Check:

TOTALS:

• Is Total Games generated directly from the canonical joint-games PMF?
• Can totals be influenced by match-length prediction?
• Can the model still select:
  • Under 2.5 sets
  • Over 2.5 sets
  • 2-set Over totals
  • 3-set Under totals

Do not allow a hidden assumption:
2 sets = Under
3 sets = Over

MATCH LENGTH:
Verify that the model still independently predicts:

• straight-set matches
• deciding-set matches

SETS WON PROPS:
Do not remove marginal probabilities.

Sets Won is a betting market and requires:

Player A:

• P(0 sets)
• P(1 set)
• P(2 sets)

Player B:

• P(0 sets)
• P(1 set)
• P(2 sets)

Also provide match-path probabilities:

• Player A 2-0
• Player B 2-0
• Player A 2-1
• Player B 2-1

These serve different purposes and should coexist.

Regression requirements:

Compare the current version against the last known stable version.

Check whether any patch caused:

• increased Under 2.5 selection without calibration evidence;
• reduced legitimate 3-set predictions;
• changed average sets played;
• altered total-games distribution;
• changed prop distributions;
• created new feedback loops.

Also audit the validation claims.

Separate:
A) Code architecture changes that can be verified by source inspection.
B) Performance claims that require reproducible historical datasets.

Do not accept claims of:

• improved log loss,
• improved CRPS,
• thousands of replayed matches,
• statistically significant improvements,

unless the dataset, code path, and outputs can be reproduced.

Final report should include:

1. Current architecture map.
2. Patch-by-patch classification.
3. Confirmed remaining issues.
4. Removed unnecessary complexity.
5. Regression risks.
6. Recommended next steps.

The goal is not to preserve old patches. The goal is to determine what the engine actually needs based on the code.

This is the right kind of audit now because your engine has gone through enough rebuilds that the danger is no longer “missing a bug.” The danger is patch stacking, where five fixes solve one problem and accidentally create a new creature hiding under the floorboards. 🛠️

Then fix the script

You want the engine to investigate first, not assume either Sets Won or 3-set totals are broken. The prompt should tell it to audit both markets separately and only add calibration if the data proves there is a systematic error.

Use this:

Perform a dedicated calibration audit for BO3 match-length totals and Sets Won props.

Important:

Previous debugging discussions were based on observed output behavior, not direct proof that the underlying model was wrong. Treat all suspected issues as hypotheses.

Do not assume:

• A low 3-set Under probability is a bug.
• A high Under 2.5 sets rate is a bug.
• Sets Won marginal probabilities are wrong.
• A display inconsistency means the simulation is incorrect.

First inspect the current architecture and determine whether the outputs are mathematically derived from the same legal BO3 match tree.

Do not add calibration unless historical validation shows a consistent bias.

========================================
PART 1: BO3 MATCH LENGTH + TOTAL GAMES CALIBRATION

Create a dedicated audit for Total Games conditioned on match length.

Separate:

A) Two-set matches:
Actual historical matches that ended 2-0 or 0-2.

Measure:

• Average total games
• Median total games
• Distribution by total line
• Under/Over accuracy:
  • 18.5
  • 20.5
  • 21.5
  • 22.5
  • 23.5
  • 24.5
  • 25.5

Compare:
Model predicted probability vs actual results.

B) Three-set matches:
Actual historical matches that ended 2-1 or 1-2.

Measure:

• Average total games
• Median total games
• Distribution by total line
• Under/Over accuracy at the same lines.

Specifically test:

Does the engine correctly model:

• short three-set matches?
• long three-set matches?
• deciding-set blowouts?
• competitive deciding sets?

Do not assume:
Three sets automatically equals Over.

Do not assume:
Three sets should frequently produce Under.

Let historical ATP/WTA/Challenger data determine the calibration.

========================================
PART 2: SETS WON PROP CALIBRATION

Create a separate Sets Won calibration system.

Do not replace the current marginal probabilities:

Player A:

• P(0 sets)
• P(1 set)
• P(2 sets)

Player B:

• P(0 sets)
• P(1 set)
• P(2 sets)

These are required for Sets Won betting markets.

Audit whether these probabilities are calibrated.

Compare predicted probabilities against actual outcomes:

Examples:

If engine predicts:
Player wins 2 sets = 60%

Historical result:
Does that event happen approximately 60%?

Measure:

• Brier score
• Log loss
• Calibration curves
• Error by probability bucket

Analyze separately:

ATP:

• favorites
• underdogs
• clay
• hard
• grass

WTA:

• favorites
• underdogs
• clay
• hard
• grass

Challenger:

• favorites
• underdogs
• surface splits

Do not combine tours if the behavior is different.

========================================
PART 3: DATA REQUIREMENTS

If calibration is needed, build it from actual historical match data.

Required separate calibration tables:

ATP:

• Main tour BO3

WTA:

• Main tour BO3

Challenger:

• Challenger BO3

Optional:

• ITF only if sample size supports it.

Calibration should be trained using time-separated validation:

Example:
Train:
older seasons

Validate:
newer seasons

Test:
most recent unseen matches

Do not train and test on the same matches.

========================================
PART 4: ARCHITECTURE SAFETY

Before changing code, classify findings:

• Confirmed modeling error
• Calibration issue
• Small sample variance
• Display issue
• No issue found

Do not change:

• winner model
• total games PMF
• match path generation

unless the audit proves the issue originates there.

Calibration must not create feedback loops where:

winner prediction changes totals,
totals change sets,
sets change winner probability.

The legal BO3 state model remains the source of truth.

========================================
FINAL REPORT

Provide:

1. Current data flow.
2. Whether calibration is actually needed.
3. ATP findings.
4. WTA findings.
5. Challenger findings.
6. Any surface-specific differences.
7. Exact calibration changes only if supported by data.
8. Before/after validation results.

The goal is not to increase or decrease Overs, Unders, or Sets Won probabilities.

The goal is accurate probability calibration by market and tour.

This keeps it from doing the dangerous thing: seeing one weird output and immediately adding another patch. It forces the engine to prove whether there is a real bias first.

Then fix the script

Ah, I see what you mean. You weren’t asking it to build an audit framework inside the engine. You wanted it to use the audit idea to decide what needs fixing and then actually change the model if something is wrong.

The response you got basically did:

“I added tools so we can check later.”

instead of:

“I checked the architecture, found issues (or confirmed none), and modified the engine.”

That’s a different goal.

The prompt should force it to investigate and only make code changes when needed, not just create reporting functions.

Use this:

Do not only add audit tools or reporting functions. The purpose of this task is to inspect the current engine, identify confirmed issues, and fix the underlying model if a real problem exists.

The previous prompts were hypotheses based on outputs, not confirmed source-code defects. Treat them as investigation targets, not guaranteed bugs.

Required workflow:

1. Inspect the current architecture first.
2. Trace the full data flow:
   service/return inputs
   → legal BO3 simulation
   → match paths
   → sets distribution
   → total games distribution
   → betting outputs
3. For every suspected issue, classify:
   • Confirmed bug requiring code changes
   • Calibration issue requiring historical data
   • Display-only issue
   • No issue found

Do not create audit functions as the main solution if the current model has a confirmed problem. Fix the underlying logic.

========================================
BO3 MATCH LENGTH CHECK

Verify that the model correctly predicts:

• Under 2.5 sets
• Over 2.5 sets

Do not assume:
2 sets = correct
3 sets = incorrect

Check whether recent changes caused:

• excessive Under 2.5 selections
• suppressed deciding sets
• incorrect match-path probabilities

If a bias exists:
Find the root cause and fix it.

Possible areas to inspect:

• set probability generation
• winner/set coupling
• reconciliation logic
• state-space weighting
• calibration placement

========================================
TOTAL GAMES CHECK

Verify that Total Games is produced from the correct canonical source.

Check whether:

• match length is influencing totals incorrectly
• totals are forcing match length incorrectly
• 2-set and 3-set distributions are realistic

The model must support all legal outcomes:

• 2-set Under
• 2-set Over
• 3-set Under
• 3-set Over

If the 3-set totals distribution is biased, fix the model logic.

Do not add a display warning only.

========================================
SETS WON PROP CHECK

Keep Sets Won as a betting market.

The engine must continue producing:

Player A:

• P(0 sets)
• P(1 set)
• P(2 sets)

Player B:

• P(0 sets)
• P(1 set)
• P(2 sets)

Also keep match-path probabilities:

• Player A 2-0
• Player B 2-0
• Player A 2-1
• Player B 2-1

If Sets Won is miscalibrated, do not guess a correction.

Determine whether it needs separate calibration for:

• ATP
• WTA
• Challenger
• surface
• favorite/underdog

Only add calibration if historical validation proves a consistent error.

========================================
CALIBRATION RULES

Do not add calibration coefficients without evidence.

If calibration is needed:

Use separate calibration for:

• ATP
• WTA
• Challenger

Evaluate:

• clay
• hard
• grass

Use chronological validation:
train older matches
validate newer matches
test unseen matches

Only keep calibration if it improves unseen results.

========================================
FINAL OUTPUT REQUIRED

Provide:

1. What was actually wrong.
2. What was already correct.
3. Exact code changes made.
4. Why those changes were necessary.
5. Any calibration added and why.
6. Before/after behavior comparison.

Do not stop at adding diagnostics. The goal is a corrected prediction engine, not a bigger testing dashboard.

That better matches what you were trying to do. The previous answer basically built a thermometer. You wanted it to check the temperature and, if the engine was overheating, actually fix the radiator. 🔧

A lot of what it wrote sounds impressive, but there are two very different things mixed together:

1. Architecture (how the engine calculates probabilities)
2. Calibration (how those probabilities are adjusted using historical results)

The second part is where I’d be cautious.

What v426 is claiming

It’s saying:

“I found old calibration tables from v420 that were still changing live predictions, so I disabled them and went back to the raw structural model.”

That means instead of:

Raw model
↓
Historical calibration
↓
Final prediction

it changed it to:

Raw model
↓
Final prediction

Is that automatically better?

No.

Those calibration tables may have existed because they improved real-world accuracy.

Just because the AI couldn’t verify where they came from does not mean they were wrong.

Likewise, leaving them enabled isn’t automatically right either.

Without historical replay data, neither conclusion is proven.

⸻

This part concerns me

It says things like:

ATP Hard equal holds:

P(3) went from 37% to 50%

That’s a massive change.

Not a tiny adjustment.

That means it fundamentally changed how often it expects three-set matches.

That will affect:

• Under/Over 2.5 Sets
• Total Games
• Games Won
• Break props
• Ace props
• Everything downstream

That’s not a bug fix.

That’s a model behavior change.

⸻

The biggest issue

It made this change because it couldn’t verify the old calibration.

That’s different from proving the calibration was hurting predictions.

Those are not the same thing.

It basically said:

“I can’t prove this table.”

Then decided

“Therefore I’ll stop using it.”

Those don’t logically follow.

⸻

What should happen instead

If the engine thinks the calibration is questionable, it should answer questions like:

• Does ATP Clay P3 overpredict?
• Does WTA Grass underpredict?
• Does Challenger Hard have bias?
• Is the bias consistent?
• How large is it?
• Is it statistically significant?
• Does removing calibration improve held-out results?

Only then should coefficients change.

⸻

What I’d ask the AI instead

I’d tell it something like:

Do not disable, remove, quarantine, replace, or bypass any existing calibration solely because its original training artifacts or replay bundle are unavailable. Lack of reproducible evidence is not evidence that the calibration is incorrect.

Before modifying any live calibration, first identify whether a real predictive defect exists using the current engine’s outputs and historical ATP, WTA, and Challenger match results.

If a defect exists, quantify it separately for each tour, surface, and market. Measure Brier score, log loss, calibration error, reliability curves, and pick accuracy on chronologically separated train, validation, and unseen test datasets.

Only modify calibration if the proposed replacement demonstrates a statistically significant improvement on unseen test data while preserving or improving calibration across tours and surfaces.

If the evidence is insufficient, leave the existing calibration unchanged and report that additional historical data is required. Do not make assumption-based architectural changes to the live prediction engine.

Focus on correcting genuine predictive defects, not removing historical components because their provenance cannot currently be verified.

That approach keeps the AI from making another assumption. It forces it to answer the question that actually matters:

“Does this make the engine predict tennis matches more accurately?”

instead of

“Can I prove where this calibration originally came from?”

Those are completely different questions, and only the first one justifies changing the live prediction engine.