Win probability

Tuesday, 30 June 2026

20 matches selected · 2 tournaments · model last ran 16:50 UTC

Umpiry rates 20 matches on Tuesday, 30 June 2026. The closest is Clara Tauson v Maria Sakkari at 50 % — the model’s most uncertain call of the day. The most one-sided is Elena Rybakina v Lois Boisson at 97 %. Probabilities come from a per-tour model trained on Elo, surface Elo, recent serve and return form and layoff.

Rated matches · ordered closest first
Time	Tournament	Round	Match	Probability	Verdict	H2H
10:05	Wimbledon WTA · grass	R128	Tauson C.Sakkari M.	50 %50 %	Toss-up	0–1
10:05	Wimbledon ATP · grass	R128	Hanfmann Y.Mpetshi Perricard G.	51 %49 %	Toss-up	2–0
10:10	Wimbledon ATP · grass	R128	Majchrzak K.Tabilo A.	52 %48 %	Toss-up	1–2
13:50	Wimbledon ATP · grass	R128	Mcdonald M.Kypson P.	52 %48 %	Toss-up	0–1
17:05	Wimbledon WTA · grass	R128	Vekic D.Krueger A.	52 %48 %	Toss-up	0–2
15:15	Wimbledon WTA · grass	R128	Mcnally C.Gabriela Ruse E.	54 %46 %	Toss-up	3–0
10:05	Wimbledon ATP · grass	R128	Shimabukuro S.Faria J.	54 %46 %	Toss-up	0–1
14:50	Wimbledon ATP · grass	R128	Moutet C.Giron M.	54 %46 %	Toss-up	1–2
14:40	Wimbledon ATP · grass	R128	Etcheverry T.Sonego L.	56 %44 %	Toss-up	0–1
10:05	Wimbledon ATP · grass	R128	Droguet T.Mannarino A.	56 %44 %	Toss-up	0–1
15:15	Wimbledon ATP · grass	R128	Fils A.Collignon R.	75 %25 %	Clear favourite	1–0
10:10	Wimbledon WTA · grass	R128	Pliskova K.Valentova T.	78 %22 %	Clear favourite	1–0
12:55	Wimbledon ATP · grass	R128	Humbert U.Bergs Z.	79 %21 %	Clear favourite	1–2
16:45	Wimbledon WTA · grass	R128	Cirstea S.Bejlek S.	83 %17 %	Strong favourite	1–0
15:10	Wimbledon ATP · grass	R128	Zverev A.Blockx A.	84 %16 %	Strong favourite	3–0
11:30	Wimbledon ATP · grass	R128	Shelton B.Virtanen O.	86 %14 %	Strong favourite	0–1
16:10	Wimbledon WTA · grass	R128	Svitolina E.Snigur D.	87 %13 %	Strong favourite	0–1
11:30	Wimbledon ATP · grass	R128	De Minaur A.Burruchaga R.	93 %7 %	Strong favourite	1–0
10:10	Wimbledon WTA · grass	R128	Anisimova A.Gjorcheska L.	96 %4 %	Strong favourite	1–0
14:20	Wimbledon WTA · grass	R128	Rybakina E.Boisson L.	97 %3 %	Strong favourite	1–0

Rated matches · ordered most one-sided first
Time	Tournament	Round	Match	Probability	Verdict	H2H
14:20	Wimbledon WTA · grass	R128	Rybakina E.Boisson L.	97 %3 %	Strong favourite	1–0
10:10	Wimbledon WTA · grass	R128	Anisimova A.Gjorcheska L.	96 %4 %	Strong favourite	1–0
11:30	Wimbledon ATP · grass	R128	De Minaur A.Burruchaga R.	93 %7 %	Strong favourite	1–0
16:10	Wimbledon WTA · grass	R128	Svitolina E.Snigur D.	87 %13 %	Strong favourite	0–1
11:30	Wimbledon ATP · grass	R128	Shelton B.Virtanen O.	86 %14 %	Strong favourite	0–1
15:10	Wimbledon ATP · grass	R128	Zverev A.Blockx A.	84 %16 %	Strong favourite	3–0
16:45	Wimbledon WTA · grass	R128	Cirstea S.Bejlek S.	83 %17 %	Strong favourite	1–0
12:55	Wimbledon ATP · grass	R128	Humbert U.Bergs Z.	79 %21 %	Clear favourite	1–2
10:10	Wimbledon WTA · grass	R128	Pliskova K.Valentova T.	78 %22 %	Clear favourite	1–0
15:15	Wimbledon ATP · grass	R128	Fils A.Collignon R.	75 %25 %	Clear favourite	1–0

How to read this

Each row gives the model’s probability that the favourite wins, and the verdict says the same thing in words. Rows are ordered by how close the model thinks the match is, so the top of the table is where it is least sure. A 70 % favourite losing is not a broken model — it is the 30 %. Probabilities are not odds and take no account of any market.

How often the model has been right

Umpiry win-probability model accuracy by tour family, measured on the predictions the model made
Tour	Correct
ATP / WTA	66 %
Challenger (men)	64 %
Challenger (women)	68 %
ITF (men)	66 %
ITF (women)	64 %

Measured on the predictions the model actually made, not on a backtest — see Methodology.

Yesterday

Monday, 29 June 2026 · 20 matches

Favourites won

16 / 20

Model expected

14.1

The model expected 14.1 favourites to win; 16 did. See yesterday’s page.

Biggest shock, last 7 days

28 Jun

Milan

CHALLENGER · clay

Alejo Sanchez Quilez beat Mili Poljicak

Surprise score

Questions

How is the probability calculated?: A separate model for each tour, trained on Elo, surface-specific Elo, recent serve and return form and time since the player’s last match. It runs several times a day; the time of the last run is at the top of this page.
How often is the model right?: We publish it. The table above gives the hit rate for each tour family, measured on the predictions the model actually made — see Methodology for how it is computed.
Why are the closest matches at the top?: Rows are ordered by distance from 50 %, so the table opens where the model is least sure. “Most one-sided” reverses the order and groups the rows by tournament.
Is this a betting tip?: No. The page shows a model’s probability and how often that model has been right. There are no odds, no comparison with a market and no suggested stake.