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Player stats 26/27

Newcastle v Brighton

Your slip follows you between games.

How often each Newcastle and Brighton player has hit the lines bookmakers price — 1+ shots on target, 2+ shots, 1+ fouls, a card — across their recent matches. How hit rates work ↓

Going into Newcastle v Brighton on Sat 30 Jan: William Osula (Newcastle) leads the home side for 1+ shots on target — 6 of 10 (60%); Jack Hinshelwood (Brighton) tops the visitors on 7 of 10 (70%). Raw hit rates from recent matches, not odds.

Predicted line-up — not the team sheet Our best guess at the shape, from recent selections. Team sheets usually land about an hour before kick-off.
What sits behind these numbers

Line-ups aren't confirmed, so the players shown are a likely XI from each club's recent selections — injuries and suspensions are filtered out where we know them. Hit rates are their recent form, not a forecast for this match. These players joined recently, and the games behind these rates were played elsewhere: Evan Ferguson (AS Roma), Femi Azeez (Millwall) — read as prior form, not this club's.

Window
Last
What a window counts

Hit rates count the most recent completed games, newest first — reaching back into earlier seasons when the current one is short, and into games for a previous club, including in another league we cover.

Market
Line
Team
How hit rates and windows work

Each row's hit rate is the share of a player's games in the window where the bet would have landed. These are 26/27 figures from completed matches: Last N is their last N games; Season is their whole 26/27; H2H is only games against this opponent.

Newcastle
Harvey BarnesF 90%9 of 10 Pro 🔒
Joe WillockM 90%9 of 10 Pro 🔒
JoelintonM 80%8 of 10 Pro 🔒
Fabian SchärD 70%7 of 10 Pro 🔒
William OsulaF 70%7 of 10 Pro 🔒
Bazoumana TouréF 70%7 of 10 Pro 🔒
Yoane WissaF 60%6 of 10 Pro 🔒
Brighton
Jack HinshelwoodM 90%9 of 10 Pro 🔒
Pascal GroßM 90%9 of 10 Pro 🔒
Kaoru MitomaM 90%9 of 10 Pro 🔒
Femi AzeezFform earned at Millwall 80%8 of 10 Pro 🔒
Diego GómezF 80%8 of 10 Pro 🔒
Evan FergusonFform earned at AS Roma 80%8 of 10 Pro 🔒
Ibrahim OsmanF 70%7 of 10 Pro 🔒
Georginio RutterF 70%7 of 10 Pro 🔒
Yankuba MintehF 60%6 of 10 Pro 🔒
Malick YalcouyéM 60%6 of 10 Pro 🔒
Pascal StruijkD 60%6 of 10 Pro 🔒
Mats WiefferD 60%6 of 10 Pro 🔒

Newcastle
Joe WillockM 70%7 of 10 Pro 🔒
William OsulaF 60%6 of 10 Pro 🔒
Brighton
Diego GómezF 70%7 of 10 Pro 🔒
Jack HinshelwoodM 70%7 of 10 Pro 🔒
Evan FergusonFform earned at AS Roma 70%7 of 10 Pro 🔒
Femi AzeezFform earned at Millwall 60%6 of 10 Pro 🔒
Ibrahim OsmanF 60%6 of 10 Pro 🔒
Kaoru MitomaM 60%6 of 10 Pro 🔒

Newcastle
No Newcastle player in this window.
Brighton
Diego GómezF 60%6 of 10 Pro 🔒

Newcastle
William OsulaF 60%6 of 10 Pro 🔒
Harvey BarnesF 60%6 of 10 Pro 🔒
Joe WillockM 60%6 of 10 Pro 🔒
Brighton
Jack HinshelwoodM 70%7 of 10 Pro 🔒
Pascal GroßM 70%7 of 10 Pro 🔒
Femi AzeezFform earned at Millwall 60%6 of 10 Pro 🔒
Evan FergusonFform earned at AS Roma 60%6 of 10 Pro 🔒

Newcastle
JoelintonM 100%10 of 10 Pro 🔒
Nico GonzálezM 70%7 of 10 Pro 🔒
Fabian SchärD 60%6 of 10 Pro 🔒
Aladji BambaM 60%6 of 10 Pro 🔒
Yoane WissaF 60%6 of 10 Pro 🔒
Brighton
Kaoru MitomaM 90%9 of 10 Pro 🔒
Yankuba MintehF 80%8 of 10 Pro 🔒
Pascal GroßM 80%8 of 10 Pro 🔒
Femi AzeezFform earned at Millwall 70%7 of 10 Pro 🔒
Chema AndrésM 70%7 of 10 Pro 🔒
Diego GómezF 60%6 of 10 Pro 🔒
Mats WiefferD 60%6 of 10 Pro 🔒
CostinhaD 60%3 of 5 Pro 🔒

Newcastle
JoelintonM 70%7 of 10 Pro 🔒
Brighton
Diego GómezF 60%6 of 10 Pro 🔒
Kaoru MitomaM 60%6 of 10 Pro 🔒

Newcastle
William OsulaF 70%7 of 10 Pro 🔒
Sven BotmanD 70%7 of 10 Pro 🔒
Bazoumana TouréF 60%6 of 10 Pro 🔒
Aladji BambaM 60%6 of 10 Pro 🔒
Jacob MurphyF 60%6 of 10 Pro 🔒
Harvey BarnesF 60%6 of 10 Pro 🔒
Joe WillockM 60%6 of 10 Pro 🔒
Brighton
Olivier BoscagliD 90%9 of 10 Pro 🔒
Kaoru MitomaM 80%8 of 10 Pro 🔒
Femi AzeezFform earned at Millwall 60%6 of 10 Pro 🔒
Jack HinshelwoodM 60%6 of 10 Pro 🔒
Ibrahim OsmanF 60%6 of 10 Pro 🔒
Malick YalcouyéM 60%6 of 10 Pro 🔒
Mats WiefferD 60%6 of 10 Pro 🔒

Newcastle
No Newcastle player in this window.
Brighton
Olivier BoscagliD 60%6 of 10 Pro 🔒

Newcastle
Sven BotmanD 90%9 of 10 Pro 🔒
Lewis MileyD 80%8 of 10 Pro 🔒
Aladji BambaM 80%8 of 10 Pro 🔒
JoelintonM 80%8 of 10 Pro 🔒
Dan BurnD 70%7 of 10 Pro 🔒
William OsulaF 60%6 of 10 Pro 🔒
Joe WillockM 60%6 of 10 Pro 🔒
Jacob RamseyM 60%6 of 10 Pro 🔒
Brighton
Yankuba MintehF 80%8 of 10 Pro 🔒
Michael SvobodaD 80%8 of 10 Pro 🔒
Femi AzeezFform earned at Millwall 70%7 of 10 Pro 🔒
Diego GómezF 70%7 of 10 Pro 🔒
Pascal GroßM 70%7 of 10 Pro 🔒
Olivier BoscagliD 70%7 of 10 Pro 🔒
Pascal StruijkD 70%7 of 10 Pro 🔒
Kaoru MitomaM 70%7 of 10 Pro 🔒
Georginio RutterF 70%7 of 10 Pro 🔒
Jack HinshelwoodM 60%6 of 10 Pro 🔒
Lewis DunkD 60%6 of 10 Pro 🔒
Chema AndrésM 60%6 of 10 Pro 🔒
Ibrahim OsmanF 60%6 of 10 Pro 🔒
Malick YalcouyéM 60%6 of 10 Pro 🔒
Mats WiefferD 60%6 of 10 Pro 🔒

Newcastle
JoelintonM 60%6 of 10 Pro 🔒
Brighton
Diego GómezF 60%6 of 10 Pro 🔒
Yankuba MintehF 60%6 of 10 Pro 🔒
Mats WiefferD 60%6 of 10 Pro 🔒

Newcastle
William OsulaF 50%5 of 10 Stats only
Bazoumana TouréF 40%4 of 10 Stats only
Harvey BarnesF 30%3 of 10 Stats only
Brighton
Jack HinshelwoodM 40%4 of 10 Stats only
Chema AndrésM 30%3 of 10 Stats only
Pascal GroßM 30%3 of 10 Stats only
Evan FergusonFform earned at AS Roma 30%3 of 10 Stats only

Newcastle
No Newcastle player in this window.
Brighton
Diego GómezF 30%3 of 10 Stats only

Newcastle
Nick PopeG 80%8 of 10 Stats only
Brighton
No Brighton player in this window.

🔒 Model is the paid layer — the model's +EV verdict on each line, per fixture in the tool. The hit rates here are free.

Tick players above to build your own slip — it follows you across games, and you can share it from the tray below.

New to these markets? The player stats guide explains what each line means and how to read a hit rate, and the glossary defines the terms.

Match officials

Not appointed yet — referees are usually confirmed two to three days before kick-off. See the referee panel

Form is only half the picture. A player hitting a line 5 of 5 can still be poor value if the true chance is lower than the streak suggests. The SharpXI tool pairs every one of these with the model's real probability — and flags the runs it doesn't back, so you're not chasing a streak about to regress. That honest verdict is the layer we're building into the tool.

Hit rates are raw recent form from public match data, for context — not a prediction or a guarantee. 18+ — please gamble responsibly.