TennisEdge's AI model finds no betting value in J. Pegula vs S. Sorribes Tormo (Wimbledon on 2026-07-01) — the market is efficient, with J. Pegula favoured at 1.09.
Independent estimates of who wins, each from a different method. The blend is what the bot prices against the market.
| Method | J. Pegula | S. Sorribes Tormo |
|---|---|---|
| Elo ratings-based, surface-weighted |
87.4% | 12.6% |
| Machine learning 29-feature gradient-boosted model |
78.3% | 21.7% |
| Blend what the bot actually prices off |
87.4% | 12.6% |
| Market implied de-vigged from the opening odds |
87.0% | 13.0% |
We publish how well these score against the closing market, including where they lose: how accurate are tennis predictions?
| Player | Surface | Elo | Record | Matches |
|---|---|---|---|---|
| J. Pegula | hard | 2077 | 192–73 | 265 |
| S. Sorribes Tormo | hard | 1519 | 62–59 | 121 |
Our own ratings, computed from match results since 2018. Players with fewer than 5 rated matches are omitted rather than shown at a provisional starting value. Last updated Aug. 3, 2026.
J. Pegula leads 2–0 over 2 meetings. Full record.
| Date | Event | Surface | Winner | Score |
|---|---|---|---|---|
| July 1, 2026 | Wimbledon | grass | J. Pegula | 2 - 0 |
| May 4, 2022 | Madrid | clay | J. Pegula | 2 - 0 |
| L | A. Eala | 1 - 2 | Aug. 3, 2026 |
| W | D. Shnaider | 2 - 0 | Aug. 1, 2026 |
| W | A. Kalinskaya | 2 - 0 | July 31, 2026 |
| W | M. Frech | 2 - 1 | July 30, 2026 |
| L | C. Gauff | 1 - 2 | July 7, 2026 |
| L | L. Bronzetti | 1 - 2 | July 31, 2026 |
| W | C. W. Hennemann | 2 - 0 | July 29, 2026 |
| W | L. Romero Gormaz | 1 - 2 | July 27, 2026 |
| L | A. Charaeva | 1 - 2 | July 21, 2026 |
| L | A. Bondar | 2 - 0 | July 13, 2026 |
All July 1, 2026 results & picks →
Wimbledon 1/32 (grass) — J. Pegula (A) vs S. Sorribes Tormo (B) MODELS: ELO 87.4% A | ML 78.3% A | Model 87.4% A | Market 87.0% A (odds 1.09 / 7.30). Value edge +0.4% on A (negligible). ML implies B 21.7% vs market 13% — a nominal underdog edge, but ELO prices B at only 12.6%; models disagree on the dog. FORM A (Pegula): Elite grass run at Berlin — beat Siniakova (1799) 6-2 6-4, Keys (1836, two TBs, GRIND), Sabalenka (1931, ELITE) in 3, lost Final to Noskova (1853) 6/21. Wimbledon R1 W vs Vidmanova (1622) 7-5 6-3. Proven she can grind deep on grass vs top-tier fields. FORM B (Sorribes): Clay/ITF grinder. Last 6 wks all on clay (W75 Portoroz title, Makarska SF). Grass sample = one R1 W vs Jimenez Kasintseva (1569) 6-2 6-3. Surface mismatch: grass is her weakest, faces an in-form grass finalist. FATIGUE: A heavy — matches_14d 5, court-time 394min, back-to-back 2 (deep Berlin run + Wimbledon R1). B fresh — matches_14d 1, court-time 54min. Real rest edge to B (~340min diff), but Pegula already proved durability through Berlin. NARRATIVE: A lost Berlin Final 9d ago (outside let-down window). No title-recency or venue signal that flips this. ML's elevated read on B is most likely a streak/level artifact (ITF-clay wins) not accounting for the grass surface mismatch — qualitative read does NOT validate the underdog edge. VERDICT: PASS. A at 1.09 offers no value (market = ELO = model). The only model disagreement (ML on B) is contradicted by ELO/market and unsupported by the surface read — a clay specialist on grass vs a grass finalist is not a value dog despite the rest edge. No actionable edge either way.