The problem is your previous prompt is still too f...

The problem is your previous prompt is still too forgiving. It keeps telling the AI “investigate” and “this doesn’t necessarily prove it’s wrong.” A code agent will often interpret that as “there may not be anything to fix.” If your goal is to make the engine internally self-consistent, the prompt needs to tell it to find and eliminate the source of contradictions, not simply audit them.

I’d write it like this:

⸻

TENNISLOCKS ENGINE-WIDE CANONICAL CONSISTENCY REBUILD

The remaining issues are no longer display bugs. The remaining issues indicate that multiple prediction subsystems are still capable of reaching incompatible conclusions from the same simulated match.

The objective is to repair the underlying probability generation so every market is produced from one canonical simulation and every public prediction tells the same story.

Do not suppress outputs or remove markets. Repair the underlying calculations.

⸻

ISSUE 1 - TOTAL GAMES ENGINE IS NOT CONSISTENT WITH THE CANONICAL MATCH SIMULATION

This is now the highest-priority issue.

Across multiple matches, the Total Games market can recommend an Over while the rest of the engine simultaneously forecasts a match structure that naturally favors the Under.

Examples include combinations such as:

Winner strongly favored
2-set match favored
Straight-set exact score favored
No tiebreak favored
First set under favored
Most common exact totals clustered below the betting line

while the Total Games recommendation still outputs Over.

The issue is not whether any individual match finished Over or Under.

The issue is that the Total Games recommendation is capable of disagreeing with the engine’s own structural forecast.

The Total Games market should never behave like an independent opinion.

It must be the mathematical consequence of the finalized exact-score distribution.

Audit the entire Total Games pipeline.

Determine whether the Over/Under decision is being influenced by anything other than the finalized exact-score PMF.

Specifically verify whether any of the following are still affecting the decision:

Expected games (mean)
Median
Smoothed expectation
Regression adjustments
Calibration layers
Shadow pricing
Historical weighting
Logistic transforms
Legacy totals estimators
Intermediate simulation states
Tail weighting
Monte Carlo summaries before reconciliation
Any cached probability objects

The final Over/Under probability must be computed only by summing the probability mass of all legal exact-score outcomes that finish above or below the market line.

Nothing else should contribute.

For every generated match verify that

P(Over Line)

equals

Σ P(all exact scores whose total games exceed the line)

and

P(Under Line)

equals

Σ P(all exact scores whose total games remain below the line)

Any deviation indicates a second probability engine still exists.

⸻

ISSUE 2 - TOTAL GAMES DISTRIBUTION MAY BE SYSTEMATICALLY BIASED

Even if the probability pipeline becomes canonical, the underlying simulation itself may still produce inaccurate score distributions.

The engine appears capable of producing distributions where long matches receive more probability than the structural assumptions justify.

Audit the simulator itself.

Investigate whether it systematically overproduces:

7-5 sets
7-6 sets
Long service holds
Three-set recoveries
Extended deuce sequences
Close-set frequencies
Late breaks
Long return-game chains

Likewise investigate whether it underproduces:

6-2 sets
6-3 sets
Comfortable favorite wins
Early breaks
One-sided service games
Straightforward straight-set victories

These biases can shift every total upward without any display bug existing.

Inspect every transition probability that affects final game counts.

Confirm that hold probabilities, break probabilities, deuce resolution, serve-order transitions, and tiebreak entry rates produce historically realistic set-length distributions.

The objective is not to force more Unders.

The objective is to eliminate any systematic tendency to inflate match length.

⸻

ISSUE 3 - PLAYER GAMES MUST BE A PURE DERIVATIVE OF EXACT SCORES

Games Won should never be estimated independently.

Player Games must be calculated directly from the finalized exact-score distribution.

Audit the Games Won engine.

Determine whether any independent estimator still contributes probability.

Look for:

Expected-games regressions
Player-specific projections
Separate Monte Carlo summaries
Legacy game models
Mean-based estimators
Calibration overrides
Market-specific smoothing

For every player verify:

Expected Games

equals

Σ (games won in each legal score × probability of that score)

The probability of Over X.5 Games must equal the summed probability of every exact-score outcome where that player exceeds the threshold.

No approximation should exist.

No secondary estimator should exist.

⸻

ISSUE 4 - PLAYER GAMES DISTRIBUTION MAY ALSO BE STRUCTURALLY BIASED

If the Games Won market is canonical but still produces implausible probabilities, inspect the simulation rather than the display.

Determine whether the engine systematically inflates:

Losing player games
Favorite games
Three-set appearances
Competitive losing sets
7-5 losses
7-6 losses

or suppresses:

6-1 losses
6-2 losses
Comfortable straight-set defeats

If losing players consistently receive too many games, every downstream Games Won market will become biased toward Overs.

Correct the simulation itself rather than applying market-specific adjustments.

⸻

ISSUE 5 - SETS WON MUST BE RECONCILED FROM EXACT SCORES

Sets Won should never disappear.

Every exact-score outcome already defines the number of sets won.

Generate Sets Won directly from the finalized exact-score PMF.

Verify:

Expected Sets

Median Sets

Mode Sets

Probability of 0

Probability of 1

Probability of 2

Probability of 3 (BO5)

all equal the exact-score distribution.

No independent estimator should exist.

⸻

ISSUE 6 - EVERY MARKET MUST SHARE ONE CANONICAL SOURCE

The canonical dependency chain should be:

Simulation

↓

Legal Match States

↓

Exact Score PMF

↓

Winner

↓

Match Length

↓

Total Games

↓

Player Games

↓

Sets Won

↓

First Set

↓

Tiebreaks

↓

Props

↓

Fair Odds

↓

Renderer

No market may bypass this chain.

No market may reconstruct probabilities.

No market may estimate outcomes separately.

⸻

ISSUE 7 - CROSS-MARKET CONSERVATION VALIDATION

After every simulation automatically verify:

Winner ⇄ Exact Score

Winner ⇄ Match Length

Winner ⇄ Player Games

Winner ⇄ Total Games

Winner ⇄ Sets Won

Match Length ⇄ Total Games

Match Length ⇄ Tiebreaks

Exact Score ⇄ Total Games

Exact Score ⇄ Player Games

Exact Score ⇄ Sets Won

First Set ⇄ Full Match

Props ⇄ Exact Score

Every identity should reconcile within floating-point tolerance.

If any market disagrees with the canonical exact-score distribution, stop the render and identify which subsystem generated the inconsistent probability.

⸻

This version tells the AI exactly what to fix. It no longer says “maybe” or “it doesn’t prove it’s wrong.” Instead, it instructs the agent to trace every market back to the canonical exact-score probability mass function, eliminate any independent estimators, and repair the simulation if it is systematically producing too many long matches or too many games for losing players. That targets the root causes you’re seeing across matches rather than treating them as isolated display issues.

════════════════════════════════════════
🎾 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 (62.4%) | fair ML -166
Moneyline: PASS at entered prices
Model fair ML: Tereza Valentova -166 / Aliaksandra sasnovich +166

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

MATCH SUMMARY: Winner forecast: Tereza Valentova 62.7% | Match length: 2 sets
🎲 MATCH LENGTH:
Forecast: 2 sets / Under 2.5 (58.7%)
Model pick: Under 2.5 sets | probability 58.7% | 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 59.4% [Efron-Morris]
Aliaksandra sasnovich: Hold 53.8% (raw: 55.2%, adj: context/fatigue) [Efron-Morris]
Style: Tereza Valentova [Baseline/Tactician] | Aliaksandra sasnovich [Vulnerable]
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 46.2% | Aliaksandra sasnovich 40.6%
Dominance Ratio: Tereza Valentova 0.64 | Aliaksandra sasnovich 0.61
Volatility: Tereza Valentova 3.6% | Aliaksandra sasnovich 12.7% | Match: 8.1%
Elo: Tereza Valentova favored (59.7%) [KM-Elo bridge]
Source: Verified Elo_Lookup (Tereza Valentova=1783, Aliaksandra Sasnovich=1698)
Blend (Tereza Valentova): MC 62.3% | Stats 69.1% | Elo 59.7%
Analytical Hold: Tereza Valentova 63.9% | Aliaksandra sasnovich 63.4% (Newton-Keller) (small sample)

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

🎯 TOTAL GAMES:

📊 Tereza Valentova - Projections:
Games Won: OVER 12.5 (50.4%) | fair odds -102 | model-generated reference line | mean 12.4 | median 13 | mode 12
1st Set Games: OVER 5.5 (65%) | Fair -184
Sets Won: 1.42 projected | median 2 | mode 2 (62.4%) | P(0) 20.1% / P(1) 17.5% / P(2) 62.4%
Serve Games: 11.50 projected
Serve Points: 78.9 projected
Aces: UNDER 0.5 (71%) | Fair -246
Double Faults: UNDER 3.5 (56%) | Fair -126
Breaks Won: UNDER 5.5 (56%) | Fair -129
Break Points Created: UNDER 10.5 (50%) | Fair -101
BP Conversion: 49% projected (5.3 / 11.0)
Opponent BP Save: 51% projected (5.6 saved / 11.0 faced)
Opponent return: opp return 28% | gate needs player sample/opponent sample
â ï¸ 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 (55.3%) | fair odds -124 | model-generated reference line | mean 10.9 | median 12 | mode 12
1st Set Games: UNDER 5.5 (52%) | Fair -110
Sets Won: 0.99 projected | median 1 | mode 0 (38.6%) | P(0) 38.6% / P(1) 23.9% / P(2) 37.6%
Serve Games: 11.49 projected
Serve Points: 79.2 projected
Aces: OVER 0.5 (55%) | Fair -122
Double Faults: UNDER 4.5 (53%) | Fair -114
Breaks Won: OVER 4.5 (50%) | Fair -100
Break Points Created: OVER 9.5 (51%) | Fair -104
BP Conversion: 46% projected (4.7 / 10.1)
Opponent BP Save: 54% projected (5.4 saved / 10.1 faced)
Opponent return: opp return 29% | gate needs player sample/opponent sample
â ï¸ 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 (51.1%)

Tiebreak forecast: NO TIEBREAK (72.9%)
Tiebreak O/U: UNDER 0.5 (72.9%)

💰 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 58.7% | model fair -142 | confidence MID
[MATCH WINNER] FORECAST ML â Tereza Valentova: model probability 62.4% | model fair -166 | confidence MID
[PROP] Match under 0.5 Tiebreaks Played: model probability 72.9% | model fair -269 | confidence STRONG
[PROP] Tereza Valentova under 0.5 Aces: model probability 71.1% | model fair -246 | confidence STRONG
[TOTAL GAMES] OVER 20.5: model probability 60.2% | model fair -151 | confidence MID
[PROP] Tereza Valentova over 5.5 1st Set Games: model probability 64.8% | model fair -184 | confidence MEDIUM
[PROP] Tereza Valentova under 5.5 Breaks Won: model probability 56.3% | model fair -129 | confidence LOW
[PROP] Tereza Valentova under 3.5 Double Faults: model probability 55.8% | model fair -126 | confidence LOW
[PROP] Aliaksandra sasnovich over 11.5 Games Won: model probability 55.3% | model fair -124 | confidence LOW | model-generated reference line
[PROP] Aliaksandra sasnovich over 0.5 Aces: model probability 55.0% | model fair -122 | confidence LOW
PLAYER PROJECTIONS:
Tereza Valentova Sets Won: 1.4 sets (canonical exact-score PMF)
Aliaksandra sasnovich Sets Won: 1.0 sets (canonical exact-score PMF)
Note: No prop sportsbook prices were entered; displayed prop sides are model-fair forecasts only.

Plus the action board is displaying below?

Why is the action board showing g the props list again when they are already being shown also and make sure u fix the total games issues it hasn’t been fixed

The problem is your previous prompt is still too f...

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