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The biggest thing I’d change is the mindset you’re...

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سؤال

TennisLocksEngine_v414_ENGINE_WIDE_CANONICAL_AUDIT_FIX_20260716.txt

The biggest thing I’d change is the mindset you’re giving the AI.

Your current prompts are saying “audit this” and “see if there’s a problem.”

A code agent is conservative. If the math technically works, it’ll conclude “nothing is wrong.”

Instead, tell it there IS a structural bug because the canonical probability model is violating its own mathematical implications, then tell it exactly what classes of bugs create that behavior and what to rebuild.

Something like this:

⸻

TENNISLOCKS ENGINE-WIDE CANONICAL TOTAL GAMES & PLAYER GAMES REBUILD

The engine still contains structural inconsistencies within the canonical probability pipeline. These are not presentation issues. They indicate that some probability markets are not being generated from the same finalized match distribution.

Do not suppress outputs, loosen thresholds, or add display gates.

Repair the probability generation itself.

The objective is for every market to become a mathematical consequence of one finalized match simulation.

⸻

ISSUE 1 - TOTAL GAMES IS STILL OVERPRODUCING LONG MATCHES

The current engine appears to systematically assign too much probability to long match outcomes.

This is not simply a matter of one incorrect prediction.

It is a structural property of the generated probability distribution.

When the simulation favors:

  • two sets
  • no tiebreak
  • moderate favorite
  • ordinary hold rates

the Total Games distribution should naturally concentrate around routine straight-set scorelines.

Instead, the Total Games distribution is still frequently producing excessive probability above sportsbook totals.

This suggests the simulator is generating too many matches near the upper end of realistic game counts.

Do not simply lower Over probabilities.

Identify why the generated distribution itself is too long.

Investigate whether the simulation is overproducing:

  • 7-5 sets
  • 7-6 sets
  • exchanged breaks
  • late recoveries
  • extended service games
  • three-set recoveries
  • split-set scenarios
  • deuce chains
  • service holds after break exchanges
  • long-tail scorelines

Determine whether these outcomes occur more frequently than they should based on the engine’s own service probabilities.

The objective is not to make the engine favor Unders.

The objective is to produce realistic tennis score distributions.

If long scorelines occur too often inside the simulation, the Total Games market will naturally overpredict Overs regardless of how the pricing layer is written.

Fix the simulation itself.

⸻

ISSUE 2 - TOTAL GAMES IS LIKELY USING TOO MUCH OF THE DISTRIBUTION MEAN

The engine appears to give excessive influence to expected games (mean).

The sportsbook market is not settled by the expected value.

It is settled by the probability mass above and below a line.

A distribution with

Mean = 23

can still favor Under 20.5

if most probability sits around:

18

19

20

and only a small number of very long matches inflate the average.

Likewise,

Mean = 21

can still strongly favor Over if most probability sits at:

21

22

23

The engine must never allow:

Expected Games

Median

Mode

or average simulation length

to influence the betting side directly.

Only the cumulative probability mass above and below the betting line should determine the forecast.

Audit every place where:

Expected Games

Average Games

Monte Carlo Mean

Weighted Average

Expected Total

or similar statistics

can influence:

Over probability

fair odds

confidence

bet selection

or recommendation.

These values should be descriptive statistics only.

They must never become decision variables.

⸻

ISSUE 3 - TOTAL GAMES DISTRIBUTION MAY HAVE DISTORTED SHAPE

The problem may not be the center of the distribution.

It may be the shape.

Audit the entire probability mass.

Measure:

mean

median

mode

variance

skew

kurtosis

tail weight

line crossing frequencies

exact total frequencies

Compare the simulated frequencies of:

16

17

18

19

20

21

22

23

24

25

26+

against what naturally results from the engine’s own point-level probabilities.

Look specifically for:

heavy right tails

compressed middle

missing routine scores

inflated long matches

inflated near-line totals

inflated extreme totals

If the probability distribution is misshaped, every totals market built from it will inherit that bias.

Repair the shape instead of adjusting sportsbook decisions afterward.

⸻

ISSUE 4 - PLAYER GAMES IS STILL STRUCTURALLY DRIFTING

Games Won should never exist as an independent prediction.

Games Won is already implied by the exact score distribution.

For every simulated match outcome:

the winner games

the loser games

the match total

and sets won

are already known.

Generate Player Games directly from those outcomes.

Do not estimate Games Won from:

hold models

serve projections

regressions

historical averages

expected games

or independent formulas.

Instead:

simulate legal match

↓

exact score

↓

player games

↓

probability distribution

↓

market probabilities

Nothing else.

⸻

ISSUE 5 - PLAYER GAMES APPEARS TO OVERWEIGHT LOSING PATHS

Investigate whether losing players receive excessive probability on high game totals.

Common causes include:

too many 7-5 losses

too many 7-6 losses

too many deciding sets

too many competitive losses

not enough comfortable defeats

If the model predicts a player loses most of the time in two sets, the Games Won distribution should naturally reflect that.

Audit whether the losing player’s game distribution contains more upper-tail outcomes than the exact-score distribution actually supports.

Do not cap projections.

Correct the probability allocation.

⸻

ISSUE 6 - MATCH STRUCTURE IS NOT FULLY PROPAGATING

Every simulated match already determines:

winner

sets

set scores

games

player games

tiebreaks

first set

service exposure

break opportunities

Every downstream market should inherit those outcomes.

No downstream market should partially regenerate them.

Audit whether any market reconstructs probabilities using independent calculations after the canonical simulation has already finished.

If so, remove those independent calculations.

⸻

ISSUE 7 - ENFORCE SCORELINE CONSERVATION

Every exact score contributes to every downstream market.

For every legal scoreline:

6-2 6-3

6-4 7-5

7-6 6-4

6-3 3-6 6-2

etc.

calculate once:

winner

sets won

games won

total games

first-set games

tiebreak count

serve games

break totals

Then accumulate those outcomes into every probability distribution.

Never rebuild these distributions separately.

Every market should literally be different views of the exact same simulated scoreline population.

⸻

ISSUE 8 - REMOVE STRUCTURAL DOUBLE COUNTING

Investigate whether probability from the same scenario enters the Total Games market more than once.

Possible sources include:

multiple Monte Carlo layers

Markov reconciliation

analytical PMFs

heuristic weighting

empirical adjustment

calibration weighting

lane blending

consensus voting

If multiple systems contribute probability for the same outcome, long matches may become unintentionally overweighted.

The finalized distribution should contain exactly one probability for every legal scoreline.

Nothing should be added afterward.

⸻

ISSUE 9 - CALIBRATE THE SIMULATOR, NOT THE MARKET

Do not attempt to “fix” Over/Under recommendations by modifying betting logic.

Instead determine why the simulation generated those probabilities.

If Overs are systematically high,

identify which scorelines are overgenerated.

If Unders are systematically high,

identify which routine wins are overgenerated.

Adjust the underlying tennis model until realistic score frequencies emerge naturally.

The betting markets should simply read the finished distribution.

⸻

FINAL OBJECTIVE

After the rebuild:

  • Winner should be the sum of winning scorelines.
  • Sets Won should be the sum of simulated set outcomes.
  • Player Games should be the sum of simulated player games.
  • Total Games should be the sum of simulated total games.
  • First Set should be the sum of simulated first-set outcomes.
  • Tiebreaks should be the sum of simulated tiebreak outcomes.

Every market should become a different projection of the exact same canonical scoreline distribution.

If one market changes, every dependent market should automatically change with it.

There should be no independent estimators, no hidden probability reconstruction, no duplicate weighting, and no market capable of disagreeing with the finalized simulation. This ensures the engine improves predictive accuracy by correcting the underlying tennis simulation rather than masking inconsistencies in the output.

Also there’s a duplicate prop list for some reason it says action board then showing a duplicate prop list we already have we don’t need the action board prop list

════════════════════════════════════════
🎾 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:

TOTAL FORECAST: OVER 20.5 (60.2%) | model fair odds -151
Mean 23.3 | median 22 | mode 19 (8.1%) | SD 5.6 | skew 0.39 | typical 80% range 16-30 games
########################################
🎯 PLAYER PROPS & PROJECTIONS 🎯
########################################

📊 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
⚠️ 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
⚠️ 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 — 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.

    text
       		after send me the updated script

Python

python
import math, collections, itertools, statistics, numpy as np
def set_dist(hA,hB,start='A',tb_pA=0.5):
    active={(0,0,start):1.0}
    out=collections.defaultdict(float)
    for loop in range(20):
        nxt=collections.defaultdict(float)
        any_=False
        for (ga,gb,srv),pr in active.items():
            if ga==6 and gb==6:
                # approximate TB based on spw inversion later, use 0.5 now
                ns='B' if srv=='A' else 'A'
                out[(7,6,ns)] += pr*tb_pA
                out[(6,7,ns)] += pr*(1-tb_pA)
                continue
            pAG=hA if srv=='A' else (1-hB)
            ns='B' if srv=='A' else 'A'
            for a,b,p in [(ga+1,gb,pr*pAG),(ga,gb+1,pr*(1-pAG))]:
                term=(a==6 and b<=4) or (b==6 and a<=4) or (a==7 and b==5) or (b==7 and a==5)
                if term: out[(a,b,ns)]+=p
                else: nxt[(a,b,ns)]+=p; any_=True
        active=nxt
        if not any_: break
    z=sum(out.values())
    return {k:v/z for k,v in out.items()}
def match_dist(hA,hB,p_tb=0.5):
    starts=[('A',.5),('B',.5)]
    final=collections.defaultdict(float)
    for st,w in starts:
        states={(0,0,st,0,0):w}
        for setno in range(3):
            nxt=collections.defaultdict(float)
            for (sa,sb,srv,gA,gB),pr in states.items():
                sd=set_dist(hA,hB,srv,p_tb)
                for (xA,xB,ns),ps in sd.items():
                    nsa=sa+(xA>xB); nsb=sb+(xB>xA)
                    ngA=gA+xA; ngB=gB+xB
                    p=pr*ps
                    if nsa==2 or nsb==2: final[(nsa,nsb,ngA,ngB)]+=p
                    else: nxt[(nsa,nsb,ns,ngA,ngB)]+=p
            states=nxt
    z=sum(final.values())
    return {k:v/z for k,v in final.items()}
d=match_dist(.594,.538)
pwin=sum(p for (sa,sb,ga,gb),p in d.items() if sa>sb)
p3=sum(p for (sa,sb,ga,gb),p in d.items() if sa+sb==3)
mean=sum((ga+gb)*p for (sa,sb,ga,gb),p in d.items())
pmf=collections.defaultdict(float)
for (sa,sb,ga,gb),p in d.items(): pmf[ga+gb]+=p
over205=sum(p for g,p in pmf.items() if g>20.5)
pwin,p3,mean,over205,sorted(pmf.items())[:5], sorted(pmf.items())[-5:]


(0.6131249270204957,
 0.4884474027108454,
 24.027800565173724,
 0.6527539623708223,
 [(12, 0.0005357452841766181),
  (13, 0.0032147941703324196),
  (14, 0.01024618670022763),
  (15, 0.023771609100499986),
  (16, 0.043641278267184086)],
 [(35, 0.01602602945035709),
  (36, 0.006523599427850493),
  (37, 0.002692087563740011),
  (38, 0.0027821043435407493),
  (39, 0.0009542527128956058)])

الفكرة

Considering match simulation improvements

I'm looking at how the sets create a positive correlation between more straight sets and fewer split sets, without arbitrary adjustments. Introducing latent match-level dominance is an option, but the lack of calibration data could make parameters too arbitrary. I also considered if the state-space might overproduce break exchanges, but deuce chains don't seem to affect total games.

Addressing potential double counting issue

The set terminal posterior might double-count recent scores, and the transition P3 history could also double count the same matches. If we remove both, it'll align with the architecture, but results might shift in either direction, which is acceptable. I'll test with synthetic history to ensure no unintended changes.

Thought for 3m 19s

الإجابة

Updated from your uploaded v414 canonical-audit script. (TennisLocksEngine_v414_ENGINE_WIDE_CANONICAL_AUDIT_FIX_20260716.txt)

Changes made

  • Terminal score history can no longer reweight 6–0 through 7–6 probabilities.
  • Ordered-set/recovery history can no longer alter deciding-set or exact-score mass.
  • Total Games and Player Games are generated from the same joint games PMF.
  • Mean, median, mode, variance, skew and kurtosis are descriptive only.
  • Over/Under selection uses only cumulative probability mass across the line.
  • Added exact-frequency and right-tail diagnostics.
  • Added stricter conservation checks between the state space, exact scores, player games and totals.
  • Removed the duplicate Action Board forecasts, passes and player projections.
  • The Action Board now appears only when an entered sportsbook price produces a positive-EV model bet.
  • Updated engine version to v415-CANONICAL-SCORELINE-REBUILD-20260716.

The script passed JavaScript syntax validation and invariance tests confirming that extreme recent-score histories cannot reshape the canonical distribution.

Download the updated TennisLocks v415 script

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