Jessica Pegula vs Sorana Cirstea

hard wta grand_slam best of 3 tennis_wta_us_open · commence 2026-09-06 21:00:00+00:00 · completed winner: a

P(Jessica Pegula) = 1.000  |  P(Sorana Cirstea) = 0.000
live composite (last tick)

Sharp anchor — Pinnacle pre-match

Pinnacle P(Jessica Pegula) 0.709
Pinnacle P(Sorana Cirstea) 0.291
n_books observed 1 live

Sharp anchor sourced via OddsAPI event_id lookup → Pinnacle no-vig computation. (Field-id mismatch fix shipped 2026-05-02 — pinnacle capture is now the source of truth for pre-match anchoring.)

Surface ELO radar

Surface Jessica PegulaSorana Cirstea Diff (a−b)
hard 1500 (0) 1500 (0) +0
clay 1500 (0) 1500 (0) +0
grass 1500 (0) 1500 (0) +0
indoor 1500 (0) 1500 (0) +0
overall 1500 (0) 1500 (0) +0

Per-surface ELO from 24h-cached global rater. Number-in-parens = matches contributing to that rating.

Style matchup (display-only · v0 priors · NOT in headline)

Player Archetype n matches Hold % Aces / match
Jessica Pegula aggressive_baseliner 189 0.726 2.6
Sorana Cirstea aggressive_baseliner 109 0.687 2.8

Matchup adjustment: +0.00pp toward Sorana Cirstea on hard.

v0 sharp priors per tennis-betting-expert lock 2026-05-02. Display-only — NOT blended into the headline composite probability above. Promotion to wired-in pending: N≥200 archetype-tagged settled matches with closing-line capture + Brier delta ≥+0.005 vs unwired baseline + no 5pp-bucket regression. Re-evaluate post-Roland Garros (2026-06-09).

Composite adjustments (live)

AdjustmentJessica PegulaSorana CirsteaNet
Match fatigue (14d load) -2.50pp (load 5.0) -1.50pp (load 3.0) -1.00pp

Live adjustments flowed into the headline composite probability above. Surface transition gated to documented poor-history (>15% win-rate drop in first 3 surface-change matches). Fatigue: 14-day load weighted by best-of (BO5=2.0, BO3=1.0), capped ±3pp. Tennis-betting-expert RG R1 audit 2026-05-24.

Composite signal weights

SignalWeight
surface_elo 0.35
recent_form 0.18
h2h_matrix 0.16
surface_transition 0.11
match_simulator 0.10
tournament_fatigue 0.10
Sum 1.00

Locked weights per tennis-betting-expert composite design 2026-04-26. Signals flagged low_data are excluded; remaining weights renormalize proportionally. h2h overweighting noted in audit; will be revisited with a dedicated calibration pass after Roland Garros.

Live trajectory (169 ticks)

Time Set Game Score Server P(Jessica Pegula) P(Sorana Cirstea) Exp games Source
00:15:33 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.748 0.252 30.8 feed
00:16:03 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.742 0.258 30.9 feed
00:16:34 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['0', '15']} b 0.737 0.263 30.8 feed
00:17:04 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['0', '30']} b 0.773 0.227 30.5 feed
00:17:34 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['15', '30']} b 0.740 0.260 30.9 feed
00:18:05 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['15', '40']} b 0.750 0.250 30.7 feed
00:18:35 2 7 {'sets': [[6, 0]], 'current_set_games': [4, 3], 'sets_won_synthetic': True, 'point_score': ['30', '40']} b 0.747 0.253 30.5 feed
00:19:05 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.773 0.227 31.1 feed
00:19:36 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.801 0.199 30.8 feed
00:20:06 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.776 0.224 31.0 feed
00:20:36 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['30', '0']} b 0.778 0.222 30.9 feed
00:21:07 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['30', '0']} b 0.779 0.221 31.1 feed
00:21:37 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['40', '0']} b 0.782 0.218 31.1 feed
00:22:07 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['40', '0']} b 0.778 0.222 30.9 feed
00:22:38 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['40', '15']} b 0.794 0.206 31.0 feed
00:23:08 2 8 {'sets': [[6, 0]], 'current_set_games': [5, 3], 'sets_won_synthetic': True, 'point_score': ['40', '30']} b 0.797 0.203 30.9 feed
00:23:49 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.753 0.247 32.7 feed
00:24:19 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.751 0.249 32.6 feed
00:25:14 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['0', '15']} b 0.753 0.247 32.6 feed
00:25:45 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['0', '15']} b 0.736 0.264 32.6 feed
00:26:15 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['0', '30']} b 0.744 0.256 32.6 feed
00:26:46 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['0', '40']} b 0.758 0.242 32.7 feed
00:27:16 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['15', '40']} b 0.739 0.261 32.8 feed
00:27:47 2 9 {'sets': [[6, 0]], 'current_set_games': [5, 4], 'sets_won_synthetic': True, 'point_score': ['30', '40']} b 0.756 0.244 32.6 feed
00:28:17 3 10 {'sets': [[6, 0], [6, 0]], 'current_set_games': [6, 4], 'sets_won_synthetic': True, 'point_score': ['7', '12']} b 1.000 0.000 0.0 feed
01:00:47 3 10 {'sets': [[6, 0], [6, 0]], 'current_set_games': [6, 4], 'sets_won_synthetic': True, 'point_score': ['7', '12']} b 1.000 0.000 0.0 feed
01:01:48 3 10 {'sets': [[6, 0], [6, 0]], 'current_set_games': [6, 4], 'sets_won_synthetic': True, 'point_score': ['7', '12']} b 1.000 0.000 0.0 feed
01:02:48 3 10 {'sets': [[6, 0], [6, 0]], 'current_set_games': [6, 4], 'sets_won_synthetic': True, 'point_score': ['7', '12']} b 1.000 0.000 0.0 feed
01:03:48 3 10 {'sets': [[6, 0], [6, 0]], 'current_set_games': [6, 4], 'sets_won_synthetic': True, 'point_score': ['7', '12']} b 1.000 0.000 0.0 feed
01:04:48 3 10 {'sets': [[6, 0], [6, 0]], 'current_set_games': [6, 4], 'sets_won_synthetic': True, 'point_score': ['7', '12']} b 1.000 0.000 0.0 feed

Showing last 30 of 169 ticks.

Tennis variance: best-of-3 single-match outcomes are noisy. Composite + trajectory shown for transparency, not as a tout. Use closing-line comparison (vs Pinnacle anchor above) as the canonical edge metric.