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My match winner keeps selecting a match winner and...

Créé le : 1 octobre 2026

Répondu en utilisant GPT-5.6 Thinking par Chat01

Question

TennisLocks_v1729.txt

My match winner keeps selecting a match winner and it loses a lot it picks the wrong player it did for this match I keep trying to correct it without hardcoding for this match but every time I fix it makes carts higher

10/1/2026 16:06:43
════════════════════════════════════════
🎾 TENNISLOCKS 🔒
MATCH PROJECTION
PRE-MATCH REPORT
GENERATED 6:06 PM | October 1, 2026
Point, game, and set projection
════════════════════════════════════════

🎯 WTA 1000 Beijing China (OUTDOOR) | Best of 3 | Line: 21.5
Tour: WTA | Court speed (CPI): 38

────────────────────────────────────────
Dayana Yastremska vs Caty McNally
────────────────────────────────────────

💰 MODEL PICKS:

  • TOP [SETS PLAYED 2.5] UNDER | 78.4% | OFFICIAL BET
  • #2 [TOTAL GAMES] UNDER 21.5 | 68.2% | full-match games | OFFICIAL BET
  • #3 [PROP] Caty McNally OVER 0.5 Sets Won | 71.3% | fair odds -248 | OFFICIAL BET

🟡 LOW CONFIDENCE:

  • Match Winner: Caty McNally 61.1% | LOW forecast

Matchup read:
Projected hold: Dayana Yastremska 56.9% | Caty McNally 63.2%
Hold separation: Caty McNally +6.3 percentage points.
Implied return vs this serve: Dayana Yastremska 44.4% | Caty McNally 47.1%
Matchup Dominance Ratio: Dayana Yastremska 0.94 | Caty McNally 1.06
Projected break: Dayana Yastremska 36.8% | Caty McNally 43.1%
Implied return separation: Caty McNally +2.7 percentage points (1 minus opponent tree serve).
Risk: LOW | score 0.15
Pricing data: WEAK | opponent-rank samples 3/3 | trust 1.00

PLAYER FORM
┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄
Dayana Yastremska Caty McNally
Rank 111 72
Elo 1686 1758
Average Opponent Rank 62 63
Schedule Strength HARD HARD
Ace Rate 3.6% 2.7%
Recent Record 3-4 3-4
Projected Hold 56.9% 63.2%
Recent Serve Pts Won 58.5% 56.2%
Recent Return Pts Won 37.9% 39.7%
Same-Surface Serve Pts 58.5% 56.2%
Matchup Serve Projection52.9% 55.6%
Implied Return Projection44.4% 47.1%
Dominance Ratio 0.94 1.06
Hold Volatility 21.6% 15.9%
Projected Break 36.8% 43.1%
First-Serve Pts Won 66.4% 60.6%
Second-Serve Pts Won 47.9% 47.6%
First-Serve In 63.1% 68.8%

MATCH WIN PROJECTION
Dayana Yastremska 38.9%
Caty McNally 61.1%
Tree serve points won: 52.9% / 55.6% | KSR_Q1_ACTIVE_POINT_OWNER_V1688
Projected winner: Caty McNally | Fair odds -157
KSR posterior-predictive owner: 4 cubature trees from the Q1 posterior | player tape count not on this Q1 (stored posterior is pre-full-tape-count; AutoFill rewrites it) | recent form sheet 7 | incremental replay 7 | tour prior 9239

POINT INPUT TELEMETRY [KSR Q1 AUTHORITY]
KSR Pre-Window (2026-08-02): Dayana Yastremska 54.0% +/- 4.1pp | Caty McNally 54.8% +/- 4.1pp
ACTIVE KSR + Current-Window Posterior: Dayana Yastremska 52.9% +/- 4.2pp | Caty McNally 55.6% +/- 4.1pp
Replay audit: player tape count not on this Q1 (stored posterior is pre-full-tape-count; AutoFill rewrites it) | recent form sheet 7 | incremental replay 7 | tour prior 9239 | current-window rows 3 / 6 | sheet rows admitted 0 / 0 | other-player bridge 456 | prior players 550 | prior through 2026-07-27
Same-tour surface window: Dayana Yastremska stored Q1 predates the full-tape count | AutoFill rewrites it | Caty McNally stored Q1 predates the full-tape count | AutoFill rewrites it
Current-window authority: UNKNOWN | -..2026-09-30 | tape rows added 0 | visible overrides 0 | target rows excluded 0 | off-surface rows kept in the serve/return walk 0
KSR hyperparameters: KSR_PREQUENTIAL_ML_RANK_FREE_PREDATED_V1693 | model UNSTAMPED | fit anchor 2026-08-01 | training rows 9239 | converged YES | deadline stop NO
Date note: 7 active replay row(s) use tournament-start date bounds.
Selected-row KSR replay trace:
#1 [B] 2026-08-02 toronto R128 | caty mcnally serve obs/pred 54.4/58.4% innov -0.166 | tatjana maria serve obs/pred 51.9/52.8% innov -0.039 | latent S/R W -0.036/0.113->-0.040/0.116 L 0.037/-0.037->0.035/-0.033
#2 [B] 2026-08-02 toronto R64 | caty mcnally serve obs/pred 60.9/54.1% innov 0.270 | linda noskova serve obs/pred 49.2/60.6% innov -0.462 | latent S/R W -0.040/0.116->-0.038/0.121 L 0.241/-0.027->0.236/-0.029
#3 [B] 2026-08-02 toronto R32 | alexandra eala serve obs/pred 54.3/54.4% innov -0.003 | caty mcnally serve obs/pred 52.2/50.3% innov 0.075 | latent S/R W -0.034/0.104->-0.034/0.103 L -0.038/0.121->-0.037/0.121
#4 [A] 2026-08-13 cincinnati R128 | tatjana maria serve obs/pred 71.1/54.7% innov 0.689 | dayana yastremska serve obs/pred 40.7/59.4% innov -0.746 | latent S/R W 0.035/-0.033->0.039/-0.025 L -0.003/0.033->-0.010/0.028
#5 [B] 2026-08-13 cincinnati R128 | caty mcnally serve obs/pred 60.0/54.7% innov 0.209 | mccartney kessler serve obs/pred 51.9/54.2% innov -0.095 | latent S/R W -0.037/0.121->-0.034/0.122 L 0.009/0.032->0.008/0.030
#6 [B] 2026-08-13 cincinnati R64 | anna kalinskaya serve obs/pred 55.2/56.7% innov -0.064 | caty mcnally serve obs/pred 49.1/53.4% innov -0.173 | latent S/R W 0.118/0.092->0.116/0.094 L -0.034/0.122->-0.037/0.123
#7 [A] 2026-08-24 monterrey R32 | janice tjen serve obs/pred 60.6/61.3% innov -0.033 | dayana yastremska serve obs/pred 57.3/58.9% innov -0.070 | latent S/R W 0.135/-0.045->0.134/-0.043 L -0.010/0.028->-0.012/0.029
Pricing owner: KSR Q1 posterior distribution. Predictive covariance is integrated into one posterior-predictive canonical PMF; Winner, Sets Played, Sets Won, and Total Games are marginals of that same mixed root.

Signals: risk: Winner evidence quality is WEAK

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

📊 DATA QUALITY

  • Sample through:
    Dayana Yastremska: through 2026-09-30 | stored Q1 predates the full-tape count | recent-form sheet is the 7-row grid | AutoFill rewrites the tape count | sheet exact date-bounded rows
    Caty McNally: through 2026-09-30 | stored Q1 predates the full-tape count | recent-form sheet is the 7-row grid | AutoFill rewrites the tape count | sheet exact date-bounded rows
  • Recent exact rows:
    Dayana Yastremska: TML exact x7
    Caty McNally: TML exact x7
    Date precision: tournament-start date x7 / tournament-start date x7
  • Point sample: verified
  • KSR Q1 point owner: 52.9% / 55.6% | player tape count not on this Q1 (stored posterior is pre-full-tape-count; AutoFill rewrites it) | recent form sheet 7 | incremental replay 7 | tour prior 9239
  • Surface SPW (HARD):
    Dayana Yastremska: stored Q1 predates the full-tape surface count | AutoFill rewrites it
    Caty McNally: stored Q1 predates the full-tape surface count | AutoFill rewrites it
    Window: full-tape count not on this Q1 | AutoFill rewrites it
  • Recent records:
    Dayana Yastremska: W-L 3-4, SS 2-2, Sets 8-9
    Caty McNally: W-L 3-4, SS 2-3, Sets 7-9

Fair total: 18.5 | median 19.0
Projected range: P10 15 | median 19 | P90 29
Expected games: 20.5 | median 19.0
Best point forecast: 19 games | most likely exact: 17g (11.3%)
Densest 3-game range: 16-18g 32.2%
Straight-set matches: average 18.1 games | Three-set matches: average 29.0 games | Three-set probability: 22%
SERVE / RETURN MATCHUP
Same-surface serve points won: 58.5% / 56.2%
Legacy current matchup SPW: 58.5% / 56.2%
ACTIVE KSR matchup SPW: 52.9% / 55.6%
Measured same-surface return points won: 37.9% / 39.7%
THREE-SET OUTLOOK
Baseline three-set probability: 37.0%
Adjusted three-set probability: 21.6%
If Dayana Yastremska wins Set 1 / if Caty McNally wins Set 1: 32.3% / 13.7%
Set-2 history increases the likelihood of straight sets.
Set-2 transition evidence: N 13.0 | branch logit shift 0.588 | variance 2.019 | precision 0.495 | rows A/B 6/7 | equal-weight within set-history horizon

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

🎯 TOTAL GAMES
Total Games 21.5: UNDER 68.2% | MEDIUM | OFFICIAL BET

Correct my script then send me is a txt file

A correction is to not make the script pass lol this used to pick Dayana but something changed it to pick the wrong player

Réponse

I’m tracing the winner from the KSR point inputs all the way into the canonical match PMF, looking for the regression that flipped the side. I’ll keep the fix structural—no player names, no match-specific exception, and no “make this example pass” override.

Réflexion

Corrected KSR matchup winner logic and searched scoped probability references

Réflexion

Inspected KSR geometry and producer logic to compute post-serve weights

Réponse

The important clue is in the script itself: recent/same-surface rows are now explicitly diagnostic, while the KSR Q1 posterior alone owns the winner. So I’m checking whether the regression is in how Q1 turns each player’s serve/return state into matchup serve probability—not in the final “pick whoever is >50%” display code.

Réflexion

Assessing the ranking shift

I’m checking whether the recent KSR change altered surface-sign handling or covariance updates. The replay data also look inconsistent with the reported window counts, which may explain why the model now ranks McNally above Yastremeska.

Assessed the ranking shift

Réflexion

Evaluating filter state

I’m examining whether reused fit state, replay timing, or omitted cross-player covariance could distort updates. Because the evidence is weak, suppressing low-confidence picks may be safer than forcing a potentially unreliable winner.

Evaluated filter state

Inspected targeted code lines and estimator regression logic

Réflexion

Reconciling row counts

I’m comparing the replay trace with the reported current-window counts. The totals don’t align, suggesting overlapping matches, alias matching, or inconsistent inclusion rules may be affecting the model’s inputs.

Investigating overlapping matches

I’m checking whether two recent head-to-head rows are counted for both players, which would explain the arithmetic: three plus six minus two equals seven unique rows and may affect replay weighting.

Checking target exclusion

I’m noticing a possible date-matching failure: if the target row’s recorded date differs from the report date, it may not be excluded correctly. I’m verifying whether stale or mismatched records are distorting the evaluation.

Investigated overlapping matches, row counts, and target exclusion

Searched 8 websites

Réflexion

Inspected approximate name matching logic and surrounding sed error lines

Réponse

I found a more serious structural issue than the final winner formula: the Q1 reader was loosened so much that it accepts an unstamped/stale KSR payload without requiring the current model revision, event, surface, round, or target date to match. Your report actually shows the symptoms—model UNSTAMPED and Current-window authority: UNKNOWN—yet that payload is still allowed to price the winner.

Réflexion

Inspected payload validation and searched mismatch logic tests

Réponse

I’m patching two places: the Preview-side Q1 validator and the producer’s Q1 read-back verification. The change will fail closed on stale/unstamped authority and force a fresh, match-bound KSR payload instead of silently pricing a winner from whatever valid-looking serve rates happen to be in Q1.

Réflexion

Patched and validated Q1 matching with targeted tests and comparisons

Réflexion

Updated script for likely Dayana candidate

Réflexion

Verified Tennis Locks integrity and repaired selection validation regression

Worked for 3m 42s

Réponse

I found a real regression in the script, not something specific to Dayana/McNally.

Your architecture says Q1 is the sole live point owner and missing evidence should fail closed. (TennisLocks_v1729.txt) But the v1716 change explicitly made schema, model revision, tour, surface, event, round, and date “not gates”—it would accept essentially any Q1 containing two valid serve rates. (TennisLocks_v1729.txt) That lines up with your report showing model UNSTAMPED and Current-window authority: UNKNOWN.

There was also a second bug: the producer supposedly verified Q1 after writing it, but the conditional could allow a failed read-back check to continue because the newly computed payload itself had valid serve rates. (TennisLocks_v1729.txt)

I changed it to v1730-Q1-STRICT-MATCH-BINDING. It now requires the current KSR model revision, schema, observation model, exact players, tour, surface, event, round, target date, and valid predictive covariance. Q1 read-back must actually pass before Preview is refreshed.

There is no Dayana/McNally hardcode, no result-based correction, no Elo/H2H winner override, and no “make this match pass” rule. I also syntax-checked the full script and tested that current payloads pass while unstamped, wrong-date, wrong-event, wrong-surface, wrong-round, and invalid-covariance payloads fail.

After replacing your script, run AutoFill once. The old unstamped Q1 should now be rejected until AutoFill creates a fresh match-bound Q1. That is intentional rather than allowing the stale authority to make a winner pick.

Download the corrected TennisLocks v1730 TXT

If a freshly rebuilt v1730 Q1 still prices McNally, then the remaining issue is inside the KSR serve/return model itself rather than stale authority handoff.

Question

TL_v1783.txt

Can u place the issues making Cary a pick ?

So “Dayana wins a set 66.1%” does not mean the model says Dayana wins a set in a Caty 2-0 outcome. It means across all four score paths, 66.1% contain at least one Dayana set.

This isn’t logical when the pick is under 2.5 both can’t win a set that’s a known tennis rule either Dayana 2:0 or 0:2 doesn’t win a set it’s that simple

Okay but Dayana clearly shoulda been the pick my model is supposed to best the books man
I don’t mean turn this into ev or something im saying that my script is still wrong

The Caty 60.6% Winner is an upstream modeling problem. I’m not going to “solve” the second one with EV, market odds, or a forced Dayana flip. I’m going back into KSR and testing the next tennis-specific layer the current model still collapses: first-serve vs second-serve matchup transfer, because Caty’s generic return latent is still being applied too broadly against Dayana.

the canonical tennis state still picks the wrong side, and the live decomposition shows exactly where: Caty’s generic return latent is still overpowering Dayana’s serve.

When Jae is using years of data it doesn’t even make sense I’ve been trying to correct this but nothing ever is affecting the match the script keeps wanting to pick the wrong match winner

Do not hardcode anything I’m trying to correct my script

10/4/2026 16:24:50
════════════════════════════════════════
🎾 TENNISLOCKS 🔒
MATCH PROJECTION
PRE-MATCH REPORT
SCRIPT v1783-KSR-PAIR-POSTERIOR-PMF-20261004
GENERATED 6:22 PM | October 4, 2026
Point, game, and set projection
════════════════════════════════════════

🎯 WTA 1000 Beijing China (OUTDOOR) | Best of 3 | Line: 21.5
Tour: WTA | Court speed (CPI): 38

────────────────────────────────────────
Dayana Yastremska vs Caty McNally
────────────────────────────────────────

💰 MODEL PICKS:

  • No official bets. Leans and low-confidence forecasts are listed below.

📊 LEANS:

  • Sets Played 2.5: UNDER 52.9%
  • Dayana Yastremska To Win A Set YES | 66.1% | NO 33.9% | LEAN
  • Caty McNally To Win A Set YES | 80.9% | NO 19.1% | LEAN

🟡 LOW CONFIDENCE:

  • Match Winner: Caty McNally 60.6% | LOW forecast
  • Total Games 21.5: UNDER 77.1% | two-set games

Matchup read:
Projected hold: Dayana Yastremska 57.6% | Caty McNally 62.8%
Hold separation: Caty McNally +5.2 percentage points.
Matchup return (= 1 - opponent SPW): Dayana Yastremska 44.7% | Caty McNally 46.9%
Matchup Dominance Ratio: Dayana Yastremska 0.95 | Caty McNally 1.05
Projected break: Dayana Yastremska 37.2% | Caty McNally 42.4%
Implied return separation: Caty McNally +2.2 percentage points (1 minus opponent tree serve).
Integrity: CAUTION | evidence flags 2
Pricing data: WEAK | KSR player rows 365/192 | predictive SE 2.6/2.6pp | archive PROVISIONAL (32 phases missing) | opponent-rank diagnostic 4/2

PLAYER FORM
┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄
Dayana Yastremska Caty McNally
Historical replay: target-bounded KSR form shown; visible/current-only details suppressed.
Rank - -
Elo - -
Average Opponent Rank - -
Schedule Strength - -
Ace Rate - -
Recent Record 2-5 4-3
Projected Hold 57.6% 62.8%
Last 7 SPW (diagnostic) 52.9% 55.5%
Last 7 RPW (diagnostic) 41.9% 45.9%
Full-Tape Serve Pts 56.8% 55.7%
Full-Tape Return Pts 43.7% 44.7%
Same-Surface Serve Pts 57.1% 55.7%
Same-Surface Return Pts 43.5% 44.1%
Matchup Serve Projection53.1% 55.3%
Matchup Return (1-opp SPW)44.7% 46.9%
Dominance Ratio 0.95 1.05
Hold Volatility - -
Projected Break 37.2% 42.4%

MATCH WIN PROJECTION
Dayana Yastremska 39.4%
Caty McNally 60.6%
Tree serve points won: 53.1% / 55.3% | KSR_Z1_CANONICAL_POINT_OWNER_V1776
Projected winner: Caty McNally | Fair odds -154
KSR point authority: committed pair 53.1% / 55.3% | posterior-predictive canonical PMF YES | available causal exact-tier tape 56178 rows | seven-row sheet is display/duplicate override only | player rows 365/192 | visible overrides 10 | causal boundary exclusions 2 | archive certification pending (32 phases missing)

POINT INPUT TELEMETRY [KSR ACTIVE AUTHORITY]
Same KSR state at two-month fit anchor (2026-09-01, information only): Dayana Yastremska 53.1% | Caty McNally 55.3%
ACTIVE CAUSAL KSR posterior: Dayana Yastremska 53.1% +/- 2.6pp | Caty McNally 55.3% +/- 2.6pp
KSR matchup logit decomposition (diagnostic, Ingram-form equation):
Dayana Yastremska: serve +0.124 - Caty McNally return +0.266 + surface +0.028 + global +0.220 + event +0.000 + pair +0.019 (2 prior) = +0.124
Caty McNally: serve +0.152 - Dayana Yastremska return +0.136 + surface -0.028 + global +0.220 + event +0.000 + pair +0.004 (2 prior) = +0.212
Return coefficients above are latent opponent-adjustment terms on the logit scale, not raw RPW percentages.
Causal replay audit: available causal exact-tier tape 56178 rows | seven-row sheet is display/duplicate override only | player rows 365/192 | visible overrides 10 | causal boundary exclusions 2 | archive certification pending (32 phases missing) | field players 0 | replay through -
Pricing-tape all-surface observed sample: Dayana Yastremska SPW 56.8% / RPW 43.7% | 365 matches | 27259 serve pts | 26362 return pts | 2016-04-18..2026-08-30 | Caty McNally SPW 55.7% / RPW 44.7% | 192 matches | 13826 serve pts | 13910 return pts | 2018-08-27..2026-08-30
Pricing-tape same-surface observed sample: Dayana Yastremska SPW 57.1% / RPW 43.5% | 238 matches | 17285 serve pts | 16867 return pts | 2016-09-26..2026-08-30 | Caty McNally SPW 55.7% / RPW 44.1% | 119 matches | 8863 serve pts | 8759 return pts | 2018-08-27..2026-08-30
Return note: observed RPW above is raw causal tape evidence; Matchup Return in PLAYER FORM is 1 minus the opponent canonical SPW and is not a second return-stat sample.
State architecture: ONE chronological exact-tier replay | separate recent/pre-window owner NONE | bridge lane NONE | seven-row sheet pricing owner NO | visible duplicate overrides 0 | causal boundary exclusions 2
Archive coverage: PROVISIONAL | TAPE_LOOKBACK_INCOMPLETE_V1774 | completed 0/32 required tour/year phases | missing 32
Set-state generation: STRUCTURAL ONLY | SET2_FULL_CAUSAL_JEFFREYS_EFFECT_CODED_ASSOCIATION_V1779 | stale 7-row/old-generation rescue DISABLED
" Visible-grid mask (diagnostic/duplicate handling only): Dayana Yastremska 6/7 accepted {""TARGET_MATCH"":1} | Caty McNally 6/7 accepted {""TARGET_MATCH"":1}"
KSR hyperparameters: TIER_CAUSAL_EXACT_BINOMIAL_SEPARATE_DRIFT_DIRECTED_PAIR_EB_V1781 | model KSR_CAUSAL_EXACT_BINOMIAL_SEPARATE_DRIFT_DIRECTED_PAIR_EB_V1781 | fit anchor 2026-09-01 | training rows 56052 | tape fingerprint 890757d4 | cache MISS | serve drift scale x0.400 | return drift scale x0.500 | one global intercept | exact binomial point-count likelihood | cache coordinate identity SAFE | symmetric covariance update | drift evals 0
KSR dynamics: exact-tier fitted serve drift variance 3.650e-3 / return 2.187e-3 per year | directed pair prior SD 0.0388 from 20160 repeated directed pairs | pair state static/no drift | 2-month serve/return random walk | career precision continuous
Date note: 56178 causal tape row(s) use tournament-start date bounds; same-event round order is used when available.
Pricing owner: one KSR authority serve walks one exact-score table. Winner and Sets are marginals of that table. Total Games prices the set-count that table selects, from the same game paths.

Signals: risk: Winner evidence quality is WEAK; causal archive certification is pending (32 required phases missing)

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

📊 DATA QUALITY

  • Sample through:
    Dayana Yastremska: through 2026-09-30 | KSR pricing tape 365 pre-match player rows | 7-row sheet is display/duplicate override only | sheet exact date-bounded rows
    Caty McNally: through 2026-09-30 | KSR pricing tape 192 pre-match player rows | 7-row sheet is display/duplicate override only | sheet exact date-bounded rows
  • Visible 7-row details (not a pricing window):
    Dayana Yastremska: TML exact x7
    Caty McNally: TML exact x7
    Date precision: tournament-start date x7 / tournament-start date x7
  • Point sample: verified
  • KSR active point owner: 53.1% / 55.3% | available causal exact-tier tape 56178 rows | seven-row sheet is display/duplicate override only | player rows 365/192 | visible overrides 10 | causal boundary exclusions 2 | archive certification pending (32 phases missing)
  • Surface SPW (HARD):
    Dayana Yastremska: 57.1% | 238 matches | 17285 serve points | 2016-09-26..2026-08-30
    Caty McNally: 55.7% | 119 matches | 8863 serve points | 2018-08-27..2026-08-30
    Window: KSR tape | same tour and surface
  • Pre-target recent records [display only; not a separate pricing window]:
    Dayana Yastremska: W-L 2-5 | pre-target KSR last 7 | SPW 52.9% | RPW 41.9% | 2026-06-08..2026-08-30
    Caty McNally: W-L 4-3 | pre-target KSR last 7 | SPW 55.5% | RPW 45.9% | 2026-07-27..2026-08-30

Fair total: 19.5 | median 19.0
Projected range: P10 16 | median 19 | P90 23
Expected games: 19.3 | median 19.0
Best point forecast: 19 games | most likely exact: 19g (13.7%)
Densest 3-game range: 18-20g 39.6%
Straight-set matches: average 19.3 games | Three-set matches: average 29.1 games | Three-set probability: 47%
SERVE / RETURN MATCHUP
ACTIVE KSR matchup SPW: 53.1% / 55.3%
THREE-SET OUTLOOK
Baseline three-set probability: 47.1%
Adjusted three-set probability: 47.1%

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

🎯 TOTAL GAMES
Total Games 21.5: UNDER 77.1% | LOW

A lot of stuff are blank that shouldn’t be that’s wiring issues I need to u to correct my script then send me the corrected script in a txt file

Réponse

I’m checking two separate things in v1783: the upstream KSR math that is giving Caty the edge, and the report wiring that is blanking fields that still have available data. I’m also tracing the set-prop calculation against the exact-score PMF so I don’t “fix” a valid tennis identity by mistake.

Réflexion

Searched TL_v1783 matchup decomposition and analysis files

Réflexion

Diagnosing model behavior

I’m checking whether the matchup equation is internally consistent. The predicted edge appears driven by a large return-latent gap that raw return points don’t support, so I’m investigating sign conventions, identifiability, and coupled updates.

Diagnosed model behavior

Réflexion

Extending diagnostic fields

I’m weighing a tape-schema expansion against deriving fields during replay. Historical as-of timing must remain intact; some blanks reflect missing source data, while others require preserving granular match statistics.

Extended diagnostic fields

Inspected KSR models, historical logic, and diagnosed Caty’s pick issues

Réponse

I’ve already found one concrete reason the winner can stay “stuck”: the report says the active KSR tape ends on August 30, even though the match is October 1 and the sheet has pre-target rows through September 30. That means the long-history state can be using years of data yet still miss the most recent month entirely; I’m tracing the union/admission code now.

Réflexion

Fixing model wiring

I’m separating the winner-selection issue from the correctly computed set probabilities. The likely culprit is stale or incomplete KSR history, so I’m tracing replay timing, player-state updates, and report wiring before editing.

Refining serve modeling

I’m identifying a structural fix: preserve first- and second-serve aggregates in unused tape fields, then estimate centered serve-type matchups within the causal filter instead of overriding the winner downstream.

Patching causal diagnostics

I’m preserving the correct set-probability identities while fixing wiring: add serve-type evidence, expose admitted history, and use target-bounded diagnostics instead of suppressing valid historical fields.

Fixed model wiring, refined serve modeling, and patched causal diagnostics

Réflexion

Inspected causal tape, telemetry, statistics, pricing, and KSR logic for wiring issues

Réponse

One point I’m not going to change: the “win a set” math. In a BO3 exact-score table, Dayana can win a set in a 1–2 loss, so her unconditional set-win probability can exceed the three-set probability. What I will add is an explicit identity check/report line so the card makes clear that Both Win A Set = Over 2.5 sets, while each player’s individual set-win probability is a different marginal.

Réflexion

Inspected player stats pipelines and implemented a backed-up KSR patch

Réponse

The second concrete defect is confirmed: v1783’s archive loader already reads first-serve/second-serve source fields, but the KSR tape collapses them into only total serve points won before modeling. So the model cannot learn “this returner is strong mainly against second serves” versus “strong against first serves”; that distinction is literally discarded upstream. I’m preserving those fields in the tape and adding a centered serve-type matchup term inside KSR, not after the winner is calculated.

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Patched KSR state logic and validated adjusted probabilities and syntax

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The patch is now structurally in place. One more wiring issue I fixed alongside it: the estimator already counted new visible rows admitted, but the committed Z1 payload threw that count away and only retained duplicate overrides. That made the audit report unable to tell whether recent rows actually entered the causal replay. v1784 carries both counts so you can see whether the “years of data” state is truly being refreshed.

Réflexion

Checked JavaScript syntax, tennis model wiring, tape validation, file differences, encoding, and execution

Réponse

The winner path itself is clean: there is no late “favorite” or rank/Elo flip. With your reported decomposition, Caty’s edge is being created before the match tree: her opponent-return term contributes about 0.130 logit more than Dayana’s, while Dayana’s surface and pair terms partly fight that back. So the correction belongs in the point observation model, exactly where I patched it—not in _winnerDecision_.

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Validated v1784 TennisLogic corrections

Réponse

The corrected file passes a full JavaScript syntax check and a small self-test. The self-test also reproduces your set numbers: 66.1%/80.9% individual set-win marginals can coexist with about 53% straight sets because the 47% split-set paths are shared; I left that math intact and added a report identity line instead.

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Verified and corrected R script integrity

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Verified corrected manuscript file availability

Worked for 9m 39s

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Yes. I corrected the upstream issues that were creating Caty’s advantage, rather than forcing Dayana to win.

The biggest problem was that your KSR model was reducing years of serve data to aggregate serve/return strength. The source architecture makes KSR the sole live point owner, with rank, Elo, and market information explicitly prohibited from overriding the Winner. (TL_v1783.txt) (TL_v1783.txt) In this matchup, Caty’s generic return latent was therefore strong enough to overwhelm Dayana’s serve even though Dayana’s underlying hard-court serving evidence was better.

I changed v1784 so that:

  • First-serve and second-serve evidence is preserved through the KSR tape instead of getting collapsed into one generic SPW number. The KSR matchup now gets a centered serve-type adjustment upstream, so a generic return coefficient cannot automatically attack every kind of serve equally.
  • Recent causal admission is now visible in telemetry. The script was calculating newly admitted visible rows but losing that count when Z1 was committed. The report will now show both newly added rows and duplicate overrides, so you can tell whether September data actually entered the state.
  • The blank historical PLAYER FORM wiring is repaired. Rank, opponent-rank strength, schedule strength, ace rate, first-serve %, first-serve points won, second-serve points won, and hold volatility are populated from target-bounded evidence when available. Historical Elo remains n/a (no as-of source) rather than silently using today's Elo.
  • The model revision and tape fingerprint were changed, so an old v1783 KSR payload cannot quietly survive into the corrected model.

On the set issue, I did not alter the probability math because that specific calculation is actually internally consistent. Your exact-score settlement defines “win a set” by summing every score path in which that player has at least one set. (TL_v1783.txt) For your displayed probabilities, an example exact-score decomposition is approximately:

Dayana 2-0 = 19.1%
Dayana 2-1 = 20.3%
Caty 2-0 = 33.9%
Caty 2-1 = 26.7%

So Dayana wins ≥1 set = 19.1 + 20.3 + 26.7 = 66.1%. Caty wins ≥1 set = 33.9 + 26.7 + 20.3 = 80.9%.

But your tennis-rule point is correct conditional on Under 2.5: if the match finishes in two sets, only the 2-0 or 0-2 outcome can occur. What was confusing was that the card displayed unconditional player-set marginals next to the Under 2.5 forecast. I added an explicit line stating:

Both Win A Set = OVER 2.5 sets
UNDER 2.5 = straight sets

That prevents those markets from looking contradictory. Your script already intends Winner, Sets and props to be marginals of the same exact-score table. (TL_v1783.txt)

I also verified there is no Dayana/Caty name check, no result hardcode, no market/EV correction, and no late Winner flip. The JavaScript passes syntax checking and the exact-score/set-event identities pass the self-test.

Important: after replacing the script, run AutoFill once. v1784 deliberately has a new KSR model revision, so it needs to rebuild the expanded causal tape and publish a fresh Z1 authority before you judge the new Winner.

Download the corrected v1784 TXT script

This does not guarantee Dayana will be selected; it removes the structural reason Caty’s broad return latent was being given too much authority. If v1784 still selects Caty after a fresh AutoFill, the next thing to audit is the learned first/second-serve return split itself rather than adding a side-specific correction.

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