You’re right. The previous version was still too h...

Question

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???
Read all instructions

Do not add gates or thresholds

Answer 1

Audit completed and engine patched

The Iasi result does not by itself prove the engine should have predicted Under 20.5 or Sasnovich Under 11.5. A 60.8% Over still loses about 39.2% of the time. However, the source did contain structural defects capable of distorting these markets.

Confirmed problems

A parallel match state was being created.
The finalized simulation used simHA and simHB, but the later exact-score state preferred statsA.spw and statsB.spw, then recalculated another hold pair. Total Games, Player Games, Sets Won, and related props could therefore represent a different match from the winner simulation. (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt) (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt)
Efron–Morris weighting was reversed.
Its sample-dependent factor increased toward one as sample size increased, but that increasing weight was applied to the population mean. Large samples were consequently shrunk more heavily than small samples. (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt)
Sets Won was suppressed by the renderer.
It was displayed only when _setsHasLineA or _setsHasLineB was true, even though a complete canonical Sets Won distribution already existed. (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt) (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt) (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt)
The 11.5 Games Won line could be generated by the model itself.
When no Games Won line was entered, the engine substituted the PMF’s generated fairLine, but the renderer presented it like a normal market line. (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt)
Service-exposure props used another hold/SPW combination.
Aces, double faults, breaks, and break points were built with _propSpwA/B and hA/hB rather than the finalized canonical state inputs. (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt)

Changes in v414

Canonical state construction now begins with the finalized effective simulation holds.
Hold and SPW values are kept mathematically coherent through inversion.
Service-exposure props now use the finalized canonical state hold/SPW pair.
Efron–Morris weighting now retains more observed signal as sample size increases.
Sets Won always appears, including:
- expected sets won;
- median;
- mode and mode probability;
- probabilities of winning 0, 1, 2, or 3 sets as applicable.
Sets Won also remains on the projection board without an entered betting line.
Generated Games Won lines are explicitly marked model-generated reference line.
Player Games displays mean, median, and mode.
Total Games displays mean, median, mode, mode mass, standard deviation, skewness, and the 80% interval.

The original Total Games settlement logic was already correctly summing exact PMF mass above and below the line. (TennisLocksEngine_v412_ONE_CANONICAL_PROBABILITY_PIPELINE_20260716.txt) I did not replace that with mean, median, mode, or heuristic decision logic.

No gates or thresholds added

Static comparison confirmed:

no new DecisionPolicy.evaluate() calls;
no new _allowBet() calls;
no new MIN_PICK_CONF usage;
no new match-winner edge thresholds;
unchanged Total Games direction formula;
unchanged Player Games direction formula.

JavaScript syntax validation passed. A synthetic canonical joint-PMF test also confirmed that Player A Games + Player B Games equals Total Games and that all three settlements originate from the same joint distribution.

Historical Brier scores, log loss, calibration curves, and ATP/WTA/Challenger/ITF backtests were not run because no historical prediction/outcome dataset or executable workbook was supplied.

Files

Download the patched v414 engine

Download the exact source diff

Download the audit report

Answer 2

You were correct about the renderer and validation architecture.

The v414 consistency check compared aggregate PMFs and expected values, but it did not independently rebuild every public market from the exact-score × player-games branches. (TennisLocksEngine_v414_ENGINE_WIDE_CANONICAL_AUDIT_FIX_20260716.txt) The Action Board was also constructed only after the detailed prop section, which forced it to the bottom, and it repeated projection-only entries inside the board. (TennisLocksEngine_v414_ENGINE_WIDE_CANONICAL_AUDIT_FIX_20260716.txt) (TennisLocksEngine_v414_ENGINE_WIDE_CANONICAL_AUDIT_FIX_20260716.txt)

v415 rebuild

Hard branch-level conservation

Before rendering, the engine now reconstructs:

P(exact match score) × P(player A games, player B games | exact match score)

It then independently derives and verifies:

Joint Player Games PMF
Player A Games PMF
Player B Games PMF
Total Games PMF
Total Games settlement at the displayed line
Player Games settlement at each displayed line
Winner probability
Match Length PMF
Sets Won PMFs
First Set PMF
Tiebreak PMF
Service-exposure identities

If any published probability differs from the reconstructed branch probability, rendering stops and names the failed identity, such as:

total line settlement pOver

The previous generic consistency failure message has been replaced with the actual subsystem and mismatch.

Total Games contradictions are now provable

The Total Games card now shows where the probability comes from:

text
Settlement decomposition:
Over 60.2% = 2 sets X% + 3 sets Y%
Under 39.8% = 2 sets X% + 3 sets Y%

This is important because:

two sets being the most probable match length;
19 being the single most probable exact total; and
Over 20.5 having more aggregate probability

can all be mathematically true simultaneously.

I did not force Total Games to agree with the mode, median, winner strength, or largest individual score category. That would create a new heuristic estimator. Instead, the engine must now demonstrate that the displayed Over or Under equals the complete branch sum.

Player Games explanation

Each Games Won projection now shows the Over composition:

text
Games Won Over composition:
win paths X% | loss paths Y% | straight-set loss Z%

For Sasnovich, this will reveal whether the Over probability comes from:

upset wins;
three-set losses;
competitive straight-set losses; or
another exact-score path.

The Games Won probability itself must exactly match the marginal of the canonical joint-games PMF.

Action Board fixed

The Action Board is now placed:

Match Winner
Match Length
Player Stats
Total Games
Action Board
Detailed Player Props and Projections
Match-Level Context

Model-generated Games Won balancing lines remain visible in the player projection cards, but they are no longer promoted to the Action Board as though they were entered sportsbook markets.

The duplicate PLAYER PROJECTIONS subsection was removed from the Action Board because Sets Won and the other projections remain fully displayed in their player cards. No market was removed.

No new gates or thresholds

The following source counts are unchanged between v414 and v415:

Rule	v414	v415
`MIN_PICK_CONF`	6	6
`MATCH_WIN_MIN_EDGE`	2	2
`_allowBet(`	2	2
`fixedActionProbabilityThreshold`	3	3
Core `pOver > 0.5` side selector	2	2

No new forecast gate, minimum probability, confidence cutoff, Over bias, Under bias, or market suppression was introduced. This preserves the requirement already stated in the uploaded v414 source. (TennisLocksEngine_v414_ENGINE_WIDE_CANONICAL_AUDIT_FIX_20260716.txt)

JavaScript syntax validation passed. A synthetic consistent branch PMF passed the new validator; altering only the displayed Total Games probability caused the expected hard failure.

Files

Download the v415 rebuilt engine

Download the exact v414 → v415 diff

Download the rebuild report

You’re right. The previous version was still too h...

问题

思考

思考

回答

Audit completed and engine patched

Confirmed problems

Changes in v414

No gates or thresholds added

Files

问题

思考

回答

v415 rebuild

Hard branch-level conservation

Total Games contradictions are now provable

Player Games explanation

Action Board fixed

No new gates or thresholds

Files

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