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🎾 TENNISLOCKS 🔒
OFFICIAL MATCH MODEL
VERSION 3.0
GENERATED 11:22 PM | September 14, 2026
ENGINE Point • Game • Set Probability Model
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🎯 WTA 500 (OUTDOOR) | Best of 3 | Line: 20.5
Tour: WTA | Court speed (CPI): 38
Metadata confidence: HIGH

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Cristina Bucsa vs Panna Udvardy
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💰 MODEL PICKS:

• TOP [TOTAL GAMES] OVER 20.5: model pick | settlement 62.5% | status OFFICIAL BET
• #2 [PROP] Panna Udvardy win 1+ set: probability 65.4% | model odds -189 | status OFFICIAL BET

📊 LEANS:

• Sets 2.5: UNDER 59.5% | MEDIUM confidence

🚫 NO BETS:

• Match Winner: NO BET | forecast Cristina Bucsa 59.3% | forecast side retained, but betting status is below OFFICIAL BET
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Match type: Mixed serve and return, close matchup. (MIXED_EVEN)
Risk: 0.00 (LOW)
Pricing data quality: STRONG | opponent-rank samples 7/7 | trust -

PLAYER INTEL
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Cristina Bucsa Panna Udvardy
Rank 44 83
Elo 1723 1685
Avg Opp Rank 75 85
Schedule A: SOLID (trust -, ranks 0) | B: SOLID (trust -, ranks 0)
Serve Style ace 1.6% ace 8.2%
Momentum RECENT_RESULTS RECENT_RESULTS
Hold % 70.3% 66.3%
Recent-row SPW (raw) 54.3% 58.8%
Dominance Ratio 0.74 0.77
Recent Hold SD 25.4% 18.1%
Break Rate 33.7% 29.7%
1st Srv Win % 58.2% 70.5%
2nd Srv Win % 45.6% 45.3%
1st Srv In % 63.0% 55.3%
Recent-row implied hold60.6% (54.3% SPW) 71.1% (58.8% SPW)

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🎲 SETS OUTLOOK
[SET RESEARCH REF] CANONICAL_POINT_ROOT | WTA/HARD/MAIN/CANONICAL_POINT_STATE_SET_COUNTS_V1144 | read-only, no live blend
[SET INPUTS] SPW A/B 58.5% / 56.7% | Hold A/B 70.3% / 66.3% | route UNIFIED_CURRENT_POINT_ROOT_V1113
[SET TB CAL] not applied | tree P(7-6) 15.0% | raw 15.0% | hist not measured | n null | CANONICAL_POINT_ROOT_NO_HISTORICAL_SET_TB_MUTATOR_V1144 | set-winner margin preserved by construction
[SET AUTHORITY] ACTIVE | BO3_PLAYER_SET_COVERAGE_EXACT_SCORE_AUTHORITY_V1155 | BO3 length priced from player 1+ set coverage and reconciled to Match Winner
[BO3 COVERAGE MODEL] Sets Won semantics | P3 = coverA + coverB - 1 | no Set-1-winner persistence owner | no corpus P3 target
[BO3 COVERAGE EFFECT] canonical P3 49.2% | set-coverage P3 40.5% | delta -8.7pp
[BO3 PLAYER COVERAGE] A wins 1+ set 75.1% | B wins 1+ set 65.4% | identity 40.5%
[BO3 RECENT SET EVIDENCE] A matches 7 | straight losses 3 | sets 8-8 || B matches 7 | straight losses 3 | sets 6-11
[SET LENGTH ROOT] final Sets Won / Both Win a Set / Over 2.5 identity P3 40.5% | one exact-score PMF
[SET WINNER ALIGN] final winner error 0.0e+0 | final set-count margin error 0.0e+0
[SET EXACT PMF] 2-0 34.6% | 2-1 24.7% | 0-2 24.9% | 1-2 15.8% | final P3 40.5%
[SET ACTION] LEAN UNDER 2.5 | probability 59.5% | model fair odds -147 | MEDIUM | forecast only
[SET BETTING GATE] final exact-score PMF direction always visible | HIGH >= 60.0% = official PICK | MID 55.0%-<60.0% = LEAN | LOW >50.0%-<55.0% = forecast only | no BO3 data-quality confidence cap
[SET FAIR PRICE] Over 2.5 +147 | Under 2.5 -147
[SET TREE DIAGNOSTIC] canonical P(2) 50.8% | canonical P(3) 49.2% | canonical point/game/set tree

📊 Player Stats (Current Live-Source Audit):

• Serve/return diagnostic: ret2 A/B 54.9% / 52.9% | BP save A/B 38.8% / 59.6%
• Visible target-surface row coverage: Cristina Bucsa through 2026-09-13 [UNVERIFIED] | Panna Udvardy through 2026-09-13 [UNVERIFIED] | CURRENT POINT INPUTS ELIGIBLE
• Live row sources: Cristina Bucsa [CURRENT_EXACT_TML_V939 x7] | Panna Udvardy [CURRENT_EXACT_TML_V939 x7] | date precision A/B TOURNEY_START_DATE x7 / TOURNEY_START_DATE x7
• Surface SPW reference (HARD): Cristina Bucsa (No verified same-tour surface SPW rate) | Panna Udvardy (No verified same-tour surface SPW rate) [TA_SURFACE_SPW_RATE_UNAVAILABLE]
• Surface serve priors: Cristina Bucsa Ace 2.2% / DF 4.2% / 1stIn 63.7% | Panna Udvardy Ace 5.7% / DF 3.9% / 1stIn 55.2% [AUTOFILL_CURRENT_EXACT_THIS_TOUR_364D_PERSISTED_V1076]
• Cristina Bucsa: Hold 70.3% (raw: 61.6%, serve vs this returner) [hold seed]
• Panna Udvardy: Hold 66.3% (raw: 72.2%, serve vs this returner) [hold seed]
• Style: Cristina Bucsa [ace 1.6% / ace 1.6%] | Panna Udvardy [ace 8.2% / ace 8.2%]
• Recent current-source results (audit): Cristina Bucsa W-L 4-3, SS 2-3, Sets 8-8 ; Panna Udvardy W-L 2-5, SS 1-3, Sets 6-11
• 1st Srv Win: Cristina Bucsa 58.2% | Panna Udvardy 70.5%
• 2nd Srv Win: Cristina Bucsa 45.6% | Panna Udvardy 45.3%
• 1st Srv In: Cristina Bucsa 63.0% | Panna Udvardy 55.3%
• Raw recent-row SPW: Cristina Bucsa 54.3% | Panna Udvardy 58.8% [diagnostic row aggregate; official pricing uses the exact-point posterior root]
• Break Rate (from hold): Cristina Bucsa 33.7% | Panna Udvardy 29.7%
• Dominance Ratio: Cristina Bucsa 0.74 | Panna Udvardy 0.77
• Recent Hold SD: Cristina Bucsa 25.4% | Panna Udvardy 18.1% | Match: 21.7%
• Elo (diagnostic only; not official serve authority): Cristina Bucsa 55.4%
  Source: Elo_Lookup sheet (Cristina Bucsa=1723, Panna Udvardy=1685)
• Serve vs this returner (Cristina Bucsa): 59.3% | Elo 55.4% (calibrates official serve when induce fires)
• Recent-row implied hold (diagnostic): Cristina Bucsa 60.6% (SPW 54.3%) | Panna Udvardy 71.1% (SPW 58.8%) (small sample)

Totals Fair Line (canonical structural threshold ref): 22.5 (CDF 50/50) | Full-dist median ref: 22.0
Full-dist range (pricing ref): P10=17 | P50=22 | P90=32
Totals EV (tree mean): 23.6 | Median: 22.0
Projected match duration: ~112 min | 2 sets ~91 min / 3 sets ~143 min | research projection only
Settlement full-dist mode: 19g | settlement density zone: 18-20g 24.0%
All-match median ref: 22.0g | Conditional totals (not picks): E[T|2 sets] 19.7 | E[T|3 sets] 29.5 | alternative 3-set probability 40%
Settlement PMF top exacts: 19g 8.4% | 20g 7.8% | 22g 7.8% | 18g 7.8% | 17g 6.5% | 21g 5.9% | 23g 5.3% | 29g 4.6% [canonical full-match mixture]

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🎯 TOTAL GAMES
[OFFICIAL TOTAL GAMES DECISION] PICK OVER 20.5 | 62.5% | OFFICIAL BET
Final pricing direction: OVER 62.5% from the official cumulative full-match Total Games threshold probability.
Pricing method: all legal full-match score paths are summed against your Total Games line. No single exact score controls the pick.
At 20.5: Over 62.5% | Under 37.5%
Total Games probability authority: ONE canonical joint score+games PMF | no second threshold recalibration is applied after the current length root.
Set-count decomposition at 20.5:
2-set lane: 59.5% match mass | P(Over | 2 sets) 37.0% | contributes 22.0pp raw Over mass
3-set lane: 40.5% match mass | P(Over | 3 sets) 99.9% | contributes 40.5pp raw Over mass
Combined no-push P(Over 20.5) = 62.5% from all lanes.
First-server sensitivity (diagnostic only): A serves first -> Over 62.5% | B serves first -> Over 62.5% | mean-total gap 0.02g
Projected total-games distribution: fair line 22.5 | mean 23.6 | median 22 | largest single exact bucket 19g (8.4%, not a majority and not the O/U authority)
Exact-total concentration: dominant 3-game cluster 18-20g = 24.0% | cluster side UNDER at 20.5
OVER threshold mass is spread across 19 exact totals | strongest OVER exact 22g = 7.8% unconditional / 12.5% of the OVER side | effective support 20.7 totals.
Shape note: the Over mass is spread across longer matches; the densest exact totals sit under the line. Official totals still use your sheet line and the cumulative tree, not a local-cluster veto.
Unconditional pricing distribution: 80% range 17-31 | SD 5.7 | mode 19g (8.4%) | leaders 19g 8.4% | 20g 7.8% | 22g 7.8% | 18g 7.8% | 17g 6.5%
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🎯 PROP PROJECTIONS 🎯
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📊 Cristina Bucsa - Player Props:
Games Won: mean 12.3 | median 13 | mode 12 | full-match distribution
1st Set Games Won: 5.08 projected
Sets Won: LEAN 2+ SETS | 59.3% | MEDIUM
Serve Games: not requested | enter a service prop line to price
Serve Points Played: not requested | enter a service prop line to price
Serve Points Won: not requested | enter a Serve Points Won line to price
Aces: not requested | enter a Aces line to price
Double Faults: not requested | enter a Double Faults line to price
Breaks Won: not requested | enter a Breaks Won line to price
Break Points Created: not requested | enter a Break Points line to price
BP Conversion: not requested | enter a Break Points line to price
Opp BP Save: not requested | enter a Break Points line to price
Opponent Matchup: opp return 32% | context only; official pricing uses the final match tree
Historical sample: 12.3 service games | projected Games Won CV: 29%

📊 Panna Udvardy - Player Props:
Games Won: mean 11.4 | median 12 | mode 12 | full-match distribution
1st Set Games Won: 4.75 projected
Sets Won: BET 1+ SET | 65.4% | HIGH
Serve Games: not requested | enter a service prop line to price
Serve Points Played: not requested | enter a service prop line to price
Serve Points Won: not requested | enter a Serve Points Won line to price
Aces: not requested | enter a Aces line to price
Double Faults: not requested | enter a Double Faults line to price
Breaks Won: not requested | enter a Breaks Won line to price
Break Points Created: not requested | enter a Break Points line to price
BP Conversion: not requested | enter a Break Points line to price
Opp BP Save: not requested | enter a Break Points line to price
Opponent Matchup: opp return 34% | context only; official pricing uses the final match tree
Historical sample: 12.5 service games | projected Games Won CV: 34%

🎲 Match-Level Context:
Sets Played: LEAN UNDER 2.5 | P(2 sets) 59.5% / P(3 sets) 40.5% | expected 2.40 sets
3-set match / both players win a set: YES 40.5% | NO 59.5% | no betting action
Exact match-score paths: Cristina Bucsa 2-0 34.6% | Cristina Bucsa 2-1 24.7% | Panna Udvardy 2-0 24.9% | Panna Udvardy 2-1 15.8%
If forecast winner Cristina Bucsa wins: straight sets 58.4% | Panna Udvardy steals one set 41.6% conditional (24.7% of all match paths)
Individual win 1+ set: Cristina Bucsa 75.1% | Panna Udvardy 65.4% | marginal coverage only; NOT the BO3 3-set probability; includes paths where that player wins the match
Straight-set loss chance: Cristina Bucsa 24.9% | Panna Udvardy 34.6%
Games/aces/breaks use the full match tree, not a single 2-0 or 2-1 scoreline.
Surface: HARD | Tour ace reference 4.4% | double-fault reference 5.1%

SYSTEM PROMPT FOR AI AGENT: BO3 Tennis Probability Engine Audit & Debugging
Role & Mission
You are an expert Quantitative Sports Modeler and Python Engineer. Your mission is to audit, debug, and strengthen a live Best-of-3 (BO3) tennis pricing engine.
The user recently refactored the BO3 architecture to base match outcomes entirely on set-coverage probabilities (winning at least one set). While this successfully unified the exact-score PMF and Sets Won markets, several mathematical anomalies have emerged (runaway variables, negative probabilities, and overconfidence).
Your goal is to fix these anomalies without abandoning the new coverage-based architecture.

1. How the BO3 Architecture Was Changed (The New Working Baseline)
   The engine no longer uses legacy methods (e.g., corpus P3 targets, Set-2 Jeffreys shrinkers, set-winner-mass tilts, or post-PMF patches). Instead, BO3 length and exact scores are derived directly from a player’s probability to avoid a sweep (win 1+ set).
   The Root Geometry:
   ⁠coverA⁠ = \bm{P(\text{Player A wins} \ge 1 \text{ set})}
   ⁠coverB⁠ = \bm{P(\text{Player B wins} \ge 1 \text{ set})}
   ⁠P3⁠ (Over 2.5 Sets) = \bm{\text{coverA} + \text{coverB} - 1}
   ⁠P2⁠ (Under 2.5 Sets) = \bm{1 - \text{P3}}
   The Exact Score Reconciliation:
   The four exact-score probabilities are solved to preserve the independent Match Winner marginals exactly:

How Coverage is Calculated:
Instead of a separate coefficient, the "win 1+ set" probability is rebuilt from the point-state mixture itself:

1. Simulate Set 1.

2. Condition the latent point-strength states (SPW/RPW) on the hypothetical Set-1 score.

3. Re-run Set 2 from that posterior state.

4. Sum the two sweep probabilities to find engine coverage: \bm{P3 = 1 - P(2\text{-}0_{sim}) - P(0\text{-}2_{sim})}.

5. This is blended with target-surface form, combining observed sweep avoidance (\bm{1 - \text{straightLosses}/\text{matches}}) and recent set-win rate converted to BO3 (\bm{1 - (1 - \text{setWinRate})^2}), using a \bm{\sqrt{\text{set count}}} weight discount.

6. The Anomalies to Investigate and Fix
   Despite the clean architecture, the integration of point-state conditioning and surface-form blending is causing cascading mathematical failures. You must investigate and fix the following four anomalies:
   Anomaly A: Posterior SPW Spikes (The Root Cause)
   The Issue: The Serve Points Won (SPW) metric occasionally becomes absurdly high during the simulation phase.
   Where to Look: Look at the conditioning step between Set 1 and Set 2. If a player dominates Set 1 (e.g., 6-0 or 6-1), the Bayesian update/posterior for Set 2 is likely overreacting, pushing the latent SPW parameter to extreme boundaries (e.g., > 85%).
   The Fix: Implement tighter bounds or heavier prior regularization on the Set 2 point-state posterior. A strong Set 1 should improve Set 2 parameters, but not cause variance explosions.
   Anomaly B: P2 Over-prediction (Under 2.5 Bias)
   The Issue: The model is calling for 2-set matches far too often, generating P2 probabilities that are too high.
   Where to Look: This is a downstream effect of Anomaly A. When posterior SPW spikes, the model thinks the Set 1 winner will easily sweep Set 2, inflating \bm{P(2\text{-}0)} and \bm{P(0\text{-}2)}, which artificially drives up P2. Additionally, check the surface form calculation: the IID assumption \bm{1 - (1 - q)^2} may be systematically underestimating set-split correlations.
   The Fix: Constrain the sweep probabilities from the simulation and introduce a correlation discount to the surface form math so that P3 isn't artificially suppressed.
   Anomaly C: Match Winner Overconfidence
   The Issue: Match Winner probabilities have become highly polarized/overconfident since this update.
   Where to Look: Look at the exact score formula: \bm{P(2\text{-}1) = P(\text{A wins}) - P(2\text{-}0)}. If the engine is producing an artificially inflated \bm{P(2\text{-}0)} (due to Anomaly A), the only way the solver can prevent \bm{P(2\text{-}1)} from going negative is by artificially dragging the Match Winner \bm{P(\text{A wins})} upward.
   The Fix: Decouple Match Winner from being forced upward by the sweep probability. Ensure Match Winner serves as an absolute anchor, and \bm{P(2\text{-}0)} is capped mathematically so it never exceeds \bm{P(\text{A wins})}.
   Anomaly D: Zero-Set Truncation (0 Coverage Edge Cases)
   The Issue: In some matchups, the model outputs a scenario where a player is predicted to not win a set at all (Coverage drops to 0, or exact scores break).
   Where to Look: Look at the post-blend step where form and simulation mix. Small sample sizes (\bm{N=7} matches) combined with a bad point-state simulation can drag ⁠cover⁠ below the player's Match Winner probability.
   The Fix: Implement strict Fréchet bounds. A player's coverage (chance to win \bm{\ge 1} set) must be strictly greater than or equal to their chance to win the match. Add a post-blend clamp: ⁠coverA = max(coverA, match_win_prob_A)⁠.

7. Strict Guardrails for Missing Data (⁠NO_CURRENT_POINT_PAIR⁠)
   The Issue: The pipeline occasionally crashes or generates fabricated probabilities when a player lacks measured SPW/RPW data.
   The Fix: You must harden the missing data guardrails. If the system detects ⁠[NO_CURRENT_POINT_PAIR]⁠ for at least one player:

8. Do NOT fall back to \bm{0.50} default point probabilities.

9. Do NOT fabricate coverage stats.

10. The function must immediately log ⁠[DATA] MATCH PREVIEW UNPRICED⁠ and cleanly return/pass without attempting to solve the BO3 matrix.

11. Execution Steps for the AI Agent

12. Audit the State Transition: Review the code handling the transition from Set 1 -> Set 2. Apply a variance clamp to prevent SPW from spiking to extreme levels based on a single hypothetical 6-0 set.

13. Apply Axiomatic Clamps: Inject bounding logic into the exact-score solver. Ensure \bm{P(2\text{-}0) \le P(\text{A wins})} and \bm{\text{coverA} \ge P(\text{A wins})}.

14. Fix Form Weighting: Review the \bm{1 - (1 - \text{setWinRate})^2} logic. Ensure small sample sizes (\bm{N < 10}) don't completely override the simulation's baseline coverage.

15. Posterior SPW Spikes ("The Blowout Bias")
    What went wrong: Your model is overreacting to small sample sizes when conditioning Set 2 probabilities on Set 1 outcomes.
    The Math: A 6-0 or 6-1 set is exceptionally short—often only 20 to 30 total serve points. If Player A wins a set 6-0, they may have won 12 out of 14 points on their serve (85% SPW). If your Bayesian updater feeds that 85% directly into Set 2 without enough resistance, the Markov chain breaks. In tennis modeling, shifting a player's SPW from an average of 64% up to 75%+ changes their probability of holding serve from ~80% to over 95%.
    The Fix: You need Bayesian shrinkage. Your prior (their long-term SPW on that surface) must be heavily weighted so that 15 points of dominant Set 1 serving only nudges their Set 2 SPW up by a fraction of a percent, rather than dragging the posterior all the way to the in-match average.

16. P2 Over-prediction (The Under 2.5 Sets / Sweep Bias)
    What went wrong: Your model assumes that sets are Independent and Identically Distributed (i.i.d.), which causes it to price 2-0 sweeps too frequently.
    The Math: If your model calculates that Player A has a 60% chance to win any given set, basic i.i.d. math dictates a 2-0 sweep happens 36% of the time (\bm{0.60 \times 0.60}). However, quantitative research by Klaassen and Magnus (2001) famously proved that tennis points and sets are highly non-stationary. Players who win Set 1 often experience a subconscious dip in intensity, while the loser plays with elimination urgency.
    The Fix: You must introduce a negative autocorrelation or "momentum penalty" between sets. If Player A wins Set 1, their baseline probability to win Set 2 should be slightly discounted to reflect real-world mean reversion, which will properly inflate your Over 2.5 Sets / 2-1 exact score pricing. 

17. Match Winner Polarization via Coverage Constraints
    What went wrong: You are forcing your Match Winner odds to be the sum of your exact score odds, allowing a bloated Set betting market to wag the dog.
    The Math: You are likely enforcing the linear constraint:

If your model suffers from Anomaly #2 (inflated 2-0 predictions), and you build your Match Winner probability strictly from the bottom up, that inflated \bm{P(2-0)} will artificially drag your overall Match Winner odds into extreme, unbettable polarization (e.g., pricing a -150 favorite as a -300 favorite).
The Fix: Top-down reconciliation. Model the overall Match Winner \bm{P(A_{match})} independently using Elo, surface yield, and H2H. Then, use your point-state model to distribute that top-line probability into the exact score buckets, rather than letting the exact score buckets dictate the top-line price.
4. Zero-Set Truncation & Negative Probabilities
What went wrong: Your blended sub-models are operating independently and violating the axioms of probability.
The Math: If you use one algorithm to predict Match Winner \bm{P(A)} and a separate, unconstrained algorithm to predict a Set 1 win or a 2-0 sweep \bm{P(A_{2-0})}, variance will inevitably cause the subset to outprice the superset. If the model outputs \bm{P(A) = 0.55} but \bm{P(A_{2-0}) = 0.60}, calculating the 2-1 score yields:

The Fix: Mathematical bounding. You must enforce the rule that \bm{P(A_{2-0}) \le P(A)} at the code level. If the raw outputs violate this, you need a normalization function (like a Softmax applied to the constrained bounds) to force the exact scores to fit inside the Match Winner envelope.
5. The 0.50 SPW Imputation Trap
What went wrong: You imputed missing Serve Points Won data with a raw 0.50 (50%) mathematical default, which destroys the variance structure of the sport.
The Math: In the ATP, average SPW is ~64%. At 64%, a server holds their game about 80% of the time. If you input 0.50 for a player with missing data, you are telling the Markov chain that the server has zero advantage. The model will simulate an endless string of breaks and deuces, entirely flattening the match variance and producing useless pricing.
The Fix: Apply your established data rules: If data conflicts or cannot be verified, do not guess—clearly state uncertainty or PASS. If you absolutely must simulate the match, impute missing data using the tour-level and surface-level average (e.g., 0.64 for ATP Hardcourt), never a blind 0.50.

It should not be printing under 2.5 here should be over 2.5 but somehow new fixes is dragging p3 down instead of p3 getting mass the whole point of the b03 was to fix it. Look at how the sets won code is to fix sets played it’s not that hard

The script is calling under 2.5 on the wrong matches