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S. Bejlek vs L. Siegemund — Prediction & Analysis

Eastbourne · WTA Eastbourne - 1/16-finals · June 22, 2026 · 11:00 UTC

TennisEdge's AI model finds no betting value in S. Bejlek vs L. Siegemund (Eastbourne on 2026-06-22) — the market is efficient, with L. Siegemund favoured at 1.67.

Surface grass Odds S. Bejlek 2.20 · L. Siegemund 1.67

Verdict: no bet

The model found no value at current prices — the market has this one priced about right. That verdict is part of the public record too.
Profiles: S. Bejlek · L. Siegemund · head-to-head record · Eastbourne

Model probabilities

Independent estimates of who wins, each from a different method. The blend is what the bot prices against the market.

Method S. Bejlek L. Siegemund
Elo
ratings-based, surface-weighted
13.6% 86.5%
Machine learning
29-feature gradient-boosted model
49.1% 50.9%
Blend
what the bot actually prices off
31.3% 68.7%
Market implied
de-vigged from the opening odds
43.2% 56.8%

We publish how well these score against the closing market, including where they lose: how accurate are tennis predictions?

Elo ratings

PlayerSurfaceEloRecordMatches
S. Bejlek clay1747 137–58195
L. Siegemund hard1715 70–62132

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 July 24, 2026.

Head to head

Level at 1–1 over 2 meetings. Full record.

DateEventSurfaceWinnerScore
June 23, 2026EastbournegrassS. Bejlek2 - 1
May 6, 2026RomeclayL. Siegemund2 - 0

Recent form

S. Bejlek

W Xiy. Wang 1 - 2 Aug. 2, 2026
L L. Tagger 2 - 0 July 24, 2026
W M. Timofeeva 0 - 2 July 23, 2026
W A. Blinkova 1 - 2 July 21, 2026
L C. Tauson 2 - 0 July 17, 2026

L. Siegemund

L E. Mertens 2 - 0 June 30, 2026
L S. Bejlek 2 - 1 June 23, 2026
L A. Anisimova 0 - 2 June 10, 2026
W F. Jones 2 - 0 June 9, 2026
L N. Osaka 0 - 2 May 26, 2026

All June 22, 2026 results & picks →

Full analysis

The mathematical consensus identifies a marginal 1.6% edge for Siegemund, supported qualitatively by her status as an experienced grass-courter facing a clay specialist (Bejlek) who is 0-2 on the surface. However, the significant divergence between the ELO model (13.6% for Bejlek) and the ML model (49.1%) introduces high variance, suggesting the true probability is uncertain. With a Kelly suggestion of 0.0 units and a low odds profile, the risk/reward ratio is poor.