Jakub Mensik vs Learner Tien

hard atp grand_slam best of 5 tennis_atp_us_open · commence 2026-09-06 00:20:00+00:00 · completed winner: b

P(Jakub Mensik) = 0.000  |  P(Learner Tien) = 1.000
live composite (last tick)

BO5 — elevated retirement risk

Best-of-5 matches (ATP men's Slams) historically retire at a materially higher rate than best-of-3. Per-player retirement frequency over their last 30 matches:
Jakub Mensik: 0/30 (0.0%)
Learner Tien: 0/30 (0.0%)

Sharp anchor — Pinnacle pre-match

Pinnacle P(Jakub Mensik) 0.436
Pinnacle P(Learner Tien) 0.564
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 Jakub MensikLearner Tien 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
Jakub Mensik big_server 33 0.826 12.0
Learner Tien counterpuncher 12 0.713 3.6

Matchup adjustment: +0.50pp toward Jakub Mensik 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)

AdjustmentJakub MensikLearner TienNet
Match fatigue (14d load) -2.50pp (load 5.0) -2.00pp (load 4.0) -0.50pp

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 (200 ticks)

Time Set Game Score Server P(Jakub Mensik) P(Learner Tien) Exp games Source
05:20:03 5 6 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 3], 'sets_won_synthetic': True, 'point_score': ['40', '15']} b 0.482 0.518 53.8 feed
05:20:34 5 6 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 3], 'sets_won_synthetic': True, 'point_score': ['40', '40']} b 0.489 0.510 53.8 feed
05:21:05 5 6 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 3], 'sets_won_synthetic': True, 'point_score': ['A', '40']} b 0.491 0.508 53.9 feed
05:21:35 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.430 0.570 54.8 feed
05:22:06 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.440 0.560 54.8 feed
05:22:36 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.427 0.573 54.8 feed
05:23:07 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.432 0.568 54.8 feed
05:23:38 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['0', '15']} b 0.423 0.577 54.7 feed
05:24:08 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['0', '30']} b 0.425 0.575 54.8 feed
05:24:39 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['15', '30']} b 0.434 0.566 54.8 feed
05:25:09 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['30', '30']} b 0.447 0.553 54.7 feed
05:25:40 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['30', '40']} b 0.429 0.571 54.7 feed
05:26:11 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['40', '40']} b 0.428 0.572 54.9 feed
05:26:41 5 7 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [3, 4], 'sets_won_synthetic': True, 'point_score': ['40', 'A']} b 0.433 0.567 54.7 feed
05:27:12 5 8 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 4], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.478 0.522 55.8 feed
05:27:42 5 8 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 4], 'sets_won_synthetic': True, 'point_score': ['15', '0']} b 0.506 0.494 55.8 feed
05:28:13 5 8 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 4], 'sets_won_synthetic': True, 'point_score': ['30', '15']} b 0.498 0.502 55.8 feed
05:28:43 5 8 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 4], 'sets_won_synthetic': True, 'point_score': ['40', '15']} b 0.501 0.499 55.7 feed
05:29:14 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.427 0.573 56.8 feed
05:29:45 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.418 0.582 56.8 feed
05:30:15 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.445 0.555 56.8 feed
05:30:46 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.435 0.565 56.8 feed
05:31:16 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['0', '0']} b 0.426 0.574 56.8 feed
05:31:47 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['15', '0']} b 0.434 0.566 56.8 feed
05:32:18 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['30', '0']} b 0.420 0.580 56.8 feed
05:32:48 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['30', '15']} b 0.429 0.571 56.7 feed
05:33:19 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['30', '15']} b 0.445 0.555 56.8 feed
05:33:49 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['40', '15']} b 0.432 0.568 56.8 feed
05:34:20 5 9 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6]], 'current_set_games': [4, 5], 'sets_won_synthetic': True, 'point_score': ['40', '30']} b 0.433 0.567 56.8 feed
05:34:50 6 10 {'sets': [[6, 0], [6, 0], [0, 6], [0, 6], [0, 6]], 'current_set_games': [4, 6], 'sets_won_synthetic': True, 'point_score': ['25', '23']} b 0.000 1.000 0.0 feed

Showing last 30 of 200 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.