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Ka. Pliskova vs T. Gibson — Prediction & Analysis

Nottingham · WTA Nottingham - Quarter-finals · June 19, 2026 · 13:30 UTC

TennisEdge's AI model finds no betting value in Ka. Pliskova vs T. Gibson (Nottingham on 2026-06-19) — the market is efficient, with Ka. Pliskova favoured at 1.57.

Surface grass Odds Ka. Pliskova 1.57 · T. Gibson 2.37

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: Ka. Pliskova · T. Gibson · head-to-head record · Nottingham

Model probabilities

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

Method Ka. Pliskova T. Gibson
Elo
ratings-based, surface-weighted
69.7% 30.3%
Machine learning
29-feature gradient-boosted model
57.5% 42.5%
Serve / return
point-by-point simulation
56.4% 43.6%
Blend
what the bot actually prices off
61.2% 38.8%
Market implied
de-vigged from the opening odds
60.2% 39.8%

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

Elo ratings

PlayerSurfaceEloRecordMatches
Ka. Pliskova hard1747 77–47124
T. Gibson hard1825 179–94273

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.

Head to head

Ka. Pliskova leads 1–0 over 1 meeting. Full record.

DateEventSurfaceWinnerScore
June 19, 2026NottinghamgrassKa. Pliskova2 - 0

Recent form

Ka. Pliskova

W O. Oliynykova 0 - 2 Aug. 3, 2026
L I. Swiatek 0 - 2 July 2, 2026
W T. Valentova 0 - 2 June 30, 2026
L M. Bouzkova 0 - 2 June 20, 2026
W T. Gibson 2 - 0 June 19, 2026

T. Gibson

W E. Cocciaretto 1 - 0 Aug. 3, 2026
L T. Maria 0 - 2 July 27, 2026
L M. Bouzkova 1 - 2 June 30, 2026
L M. Keys 0 - 2 June 23, 2026
L Ka. Pliskova 2 - 0 June 19, 2026

All June 19, 2026 results & picks →

Full analysis

The consensus model identifies a marginal 2.1% edge for Pliskova, driven primarily by the ELO model which favors her grass pedigree and massive serve advantage (61.9% service points won). However, the Machine Learning model disagrees significantly, projecting only a 57.5% win probability, which suggests the market may be correctly pricing in recent form or decline factors that ELO misses. With the Kelly criterion suggesting 0.0 units and a high 5.9% bookmaker margin, the risk-reward ratio is unfavorable for a bet.