TennisEdge's AI model finds no betting value in A. Eala vs E. Mertens (Bad Homburg on 2026-06-22) — the market is efficient, with A. Eala favoured at 1.80.
Independent estimates of who wins, each from a different method. The blend is what the bot prices against the market.
| Method | A. Eala | E. Mertens |
|---|---|---|
| Elo ratings-based, surface-weighted |
67.8% | 32.2% |
| Machine learning 29-feature gradient-boosted model |
43.6% | 56.4% |
| Serve / return point-by-point simulation |
65.6% | 34.4% |
| Blend what the bot actually prices off |
59.0% | 41.0% |
| Market implied de-vigged from the opening odds |
52.6% | 47.4% |
We publish how well these score against the closing market, including where they lose: how accurate are tennis predictions?
| Player | Surface | Elo | Record | Matches |
|---|---|---|---|---|
| A. Eala | hard | 1905 | 174–90 | 264 |
| E. Mertens | hard | 1855 | 118–77 | 195 |
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.
E. Mertens leads 3–0 over 3 meetings. Full record.
| Date | Event | Surface | Winner | Score |
|---|---|---|---|---|
| June 22, 2026 | Bad Homburg | grass | E. Mertens | 2 - 0 |
| April 24, 2026 | Madrid | clay | E. Mertens | 2 - 0 |
| Oct. 17, 2023 | WTA Monastir | hard | E. Mertens | 2 - 0 |
| W | J. Pegula | 1 - 2 | Aug. 3, 2026 |
| W | N. Osaka | 0 - 2 | Aug. 1, 2026 |
| W | E. Svitolina | 2 - 0 | July 31, 2026 |
| W | L. Fernandez | 0 - 2 | July 29, 2026 |
| W | Q. Zheng | 2 - 1 | July 28, 2026 |
| L | L. Noskova | 2 - 0 | July 8, 2026 |
| W | M. Bouzkova | 0 - 2 | July 6, 2026 |
| W | E. Rybakina | 2 - 0 | July 4, 2026 |
| W | M. Timofeeva | 2 - 1 | July 2, 2026 |
| W | L. Siegemund | 2 - 0 | June 30, 2026 |
All June 22, 2026 results & picks →
The consensus model identifies a slim 1.5% edge for Eala, primarily driven by a bullish ELO rating on grass, but the Kelly criterion correctly suggests 0 units. This marginal value is eroded by critical qualitative factors: Eala is at a distinct fatigue disadvantage having played 4 matches in the last 7 days compared to Mertens' 2, and she holds a losing 0-2 head-to-head record against the Belgian. Furthermore, the Machine Learning model strongly disagrees with the ELO prediction, flagging this as a high-variance spot where the market's 1.80 odds likely accurately reflect the true risk.