Win probability

Wednesday, 24 June 2026

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

Umpiry rates 20 matches on Wednesday, 24 June 2026. The closest is Moez Echargui v Keegan Smith at 52 % — the model’s most uncertain call of the day. The most one-sided is Robin Montgomery v Elvina Kalieva at 84 %. 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
14:25	Wimbledon ATP · grass	SF	Echargui M.Smith K.	52 %48 %	Toss-up	1–1
13:40	Wimbledon ATP · grass	SF	Holmgren A.Kym J.	53 %47 %	Toss-up	0–1
13:15	Wimbledon WTA · grass	SF	Day K.Andreeva E.	54 %46 %	Toss-up	1–1
14:55	Wimbledon ATP · grass	SF	Pellegrino A.Boyer T.	54 %46 %	Toss-up	0–2
10:10	Wimbledon WTA · grass	SF	Sawangkaew M.Stoiana M.	55 %45 %	Toss-up	2–0
16:15	Wimbledon ATP · grass	SF	Mayot H.Moro Canas A.	55 %45 %	Toss-up	0–1
13:40	Wimbledon ATP · grass	SF	Gojo B.Smith C.	55 %45 %	Toss-up	1–2
14:15	Wimbledon ATP · grass	SF	Djere L.Zheng M.	57 %43 %	Toss-up	0–1
17:50	Wimbledon WTA · grass	SF	Timofeeva M.Podrez V.	57 %43 %	Toss-up	1–0
13:15	Wimbledon ATP · grass	SF	Mcdonald M.Carballes Baena R.	57 %43 %	Toss-up	2–0
13:15	Wimbledon WTA · grass	SF	Crawley F.Riera J.	60 %40 %	Slight edge	1–0
10:10	Wimbledon ATP · grass	SF	Evans D.Schoolkate T.	60 %40 %	Slight edge	0–1
13:45	Wimbledon ATP · grass	SF	Gaston H.Cina F.	63 %37 %	Slight edge	1–0
10:10	Wimbledon ATP · grass	SF	Onclin G.Mochizuki S.	71 %29 %	Clear favourite	1–1
13:50	Wimbledon WTA · grass	SF	Kawa K.Stefanini L.	72 %28 %	Clear favourite	1–1
13:15	Wimbledon ATP · grass	SF	Blanch D.Sweeny D.	74 %26 %	Clear favourite	0–1
15:25	Wimbledon WTA · grass	SF	Zhu L.Sarah Rakotomanga Rajaonah T.	74 %26 %	Clear favourite	1–0
10:10	Wimbledon ATP · grass	SF	Heide G.Mejia N.	77 %23 %	Clear favourite	1–1
15:35	Wimbledon ATP · grass	SF	Woo Kwon S.Gea A.	78 %22 %	Clear favourite	1–1
17:25	Wimbledon WTA · grass	SF	Montgomery R.Kalieva E.	84 %16 %	Strong favourite	2–2

Rated matches · ordered most one-sided first
Time	Tournament	Round	Match	Probability	Verdict	H2H
17:25	Wimbledon WTA · grass	SF	Montgomery R.Kalieva E.	84 %16 %	Strong favourite	2–2
15:35	Wimbledon ATP · grass	SF	Woo Kwon S.Gea A.	78 %22 %	Clear favourite	1–1
10:10	Wimbledon ATP · grass	SF	Heide G.Mejia N.	77 %23 %	Clear favourite	1–1
15:25	Wimbledon WTA · grass	SF	Zhu L.Sarah Rakotomanga Rajaonah T.	74 %26 %	Clear favourite	1–0
13:15	Wimbledon ATP · grass	SF	Blanch D.Sweeny D.	74 %26 %	Clear favourite	0–1
13:50	Wimbledon WTA · grass	SF	Kawa K.Stefanini L.	72 %28 %	Clear favourite	1–1
10:10	Wimbledon ATP · grass	SF	Onclin G.Mochizuki S.	71 %29 %	Clear favourite	1–1

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

Tuesday, 23 June 2026 · 15 matches

Favourites won

10 / 15

Model expected

9.1

The model expected 9.1 favourites to win; 10 did. See yesterday’s page.

Biggest shock, last 7 days

23 Jun

Eastbourne

ATP · grass

R32

Giles Hussey beat Matteo Arnaldi

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.