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

생성일: 2026년 10월 1일

답변에 사용된 모델 GPT-5.6 Thinking by Chat01

질문

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
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🎾 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

답변

I’m going to trace the match-winner logic specifically—not hardcode this Yastremska/McNally match—and adjust the model so weak or conflicting winner evidence can’t become an overconfident pick while leaving your totals/sets calibration intact as much as possible. Then I’ll return the corrected script as a .txt file.

사고

Searched for a script to modify tennis match predictions

Worked for 7s

답변

I can fix it, but the actual script is not attached in this conversation—only the model output is. I need the .py, .txt, or pasted source code to make a real correction and return the full corrected script as a .txt.

From this report, I already see the likely failure mode: your match-winner probability is trusting the KSR point projection too aggressively even when winner evidence is explicitly WEAK/UNKNOWN. Here McNally gets 61.1% largely from 52.9% vs 55.6% projected serve points, despite:

  • Pricing data: WEAK
  • Winner evidence quality is WEAK
  • Current-window authority: UNKNOWN
  • only 3 / 6 current-window rows
  • stored Q1 predating the full-tape count
  • both players having the same 3–4 recent record
  • Yastremska having the better recent/same-surface serve-points-won number, 58.5% vs 56.2%

The correction should be general, not “pick Yastremska here”: reduce/shrink winner separation when evidence quality is weak, make posterior uncertainty affect the winner probability, prevent tiny point-level edges from becoming large match-winner edges without enough supporting data, and keep that correction isolated from the total-games/sets engine so fixing winners doesn’t automatically push totals higher.

Upload the script and I’ll edit the actual winner pipeline and give you the corrected .txt file.

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