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Player stats 25/26

Wolves v Chelsea

Your slip follows you between games.

How often each Wolves and Chelsea 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 Wolves v Chelsea: Adam Armstrong (Wolves) leads the home side for 1+ shots on target — 7 of 10 (70%); Cole Palmer (Chelsea) tops the visitors on 7 of 10 (70%). Raw hit rates from recent matches, not odds.

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.

Wolves
Adam ArmstrongMform earned at Southampton 90%9 of 10 Pro 🔒
Tolu ArokodareF 90%9 of 10 Pro 🔒
Mateus ManéM 70%7 of 10 Pro 🔒
Hwang Hee-chanM 70%7 of 10 Pro 🔒
Angel GomesMform earned at Olympique de Marseille 60%6 of 10 Pro 🔒
Chelsea
Cole PalmerM 100%10 of 10 Pro 🔒
Enzo FernándezM 80%8 of 10 Pro 🔒
Pedro NetoM 70%7 of 10 Pro 🔒
João PedroF 70%7 of 10 Pro 🔒
Liam DelapF 70%7 of 10 Pro 🔒
Alejandro GarnachoF 60%6 of 10 Pro 🔒

Wolves
Adam ArmstrongMform earned at Southampton 80%8 of 10 Pro 🔒
Chelsea
Enzo FernándezM 70%7 of 10 Pro 🔒

Wolves
Adam ArmstrongMform earned at Southampton 70%7 of 10 Pro 🔒
Chelsea
No Chelsea player in this window.

Wolves
Adam ArmstrongMform earned at Southampton 70%7 of 10 Pro 🔒
Mateus ManéM 60%6 of 10 Pro 🔒
Chelsea
Cole PalmerM 70%7 of 10 Pro 🔒
Enzo FernándezM 60%6 of 10 Pro 🔒

Wolves
Yerson MosqueraD 90%9 of 10 Pro 🔒
João GomesM 90%9 of 10 Pro 🔒
Angel GomesMform earned at Olympique de Marseille 80%8 of 10 Pro 🔒
Hwang Hee-chanM 70%7 of 10 Pro 🔒
Santiago BuenoD 70%7 of 10 Pro 🔒
Mateus ManéM 60%6 of 10 Pro 🔒
Adam ArmstrongMform earned at Southampton 60%6 of 10 Pro 🔒
Ladislav KrejčíD 60%6 of 10 Pro 🔒
Chelsea
Moisés CaicedoM 100%10 of 10 Pro 🔒
Marc CucurellaD 80%8 of 10 Pro 🔒
Wesley FofanaD 70%7 of 10 Pro 🔒
Liam DelapF 70%7 of 10 Pro 🔒
Enzo FernándezM 60%6 of 10 Pro 🔒
João PedroF 60%6 of 10 Pro 🔒

Wolves
João GomesM 60%6 of 10 Pro 🔒
Chelsea
Moisés CaicedoM 60%6 of 10 Pro 🔒

Wolves
Yerson MosqueraD 90%9 of 10 Pro 🔒
João GomesM 70%7 of 10 Pro 🔒
Mateus ManéM 70%7 of 10 Pro 🔒
Hugo BuenoD 60%6 of 10 Pro 🔒
Angel GomesMform earned at Olympique de Marseille 60%6 of 10 Pro 🔒
Chelsea
Moisés CaicedoM 90%9 of 10 Pro 🔒
Cole PalmerM 80%8 of 10 Pro 🔒
Alejandro GarnachoF 70%7 of 10 Pro 🔒
Enzo FernándezM 70%7 of 10 Pro 🔒
Marc CucurellaD 60%6 of 10 Pro 🔒
João PedroF 60%6 of 10 Pro 🔒
Liam DelapF 60%6 of 10 Pro 🔒

Wolves
Mateus ManéM 60%6 of 10 Pro 🔒
Chelsea
Cole PalmerM 60%6 of 10 Pro 🔒
Moisés CaicedoM 60%6 of 10 Pro 🔒

Wolves
Yerson MosqueraD 90%9 of 10 Pro 🔒
João GomesM 90%9 of 10 Pro 🔒
Matt DohertyD 90%9 of 10 Pro 🔒
Santiago BuenoD 80%8 of 10 Pro 🔒
Hugo BuenoD 70%7 of 10 Pro 🔒
Mateus ManéM 70%7 of 10 Pro 🔒
Rodrigo GomesD 60%6 of 10 Pro 🔒
Ladislav KrejčíD 60%6 of 10 Pro 🔒
Chelsea
Marc CucurellaD 90%9 of 10 Pro 🔒
Moisés CaicedoM 90%9 of 10 Pro 🔒
Cole PalmerM 80%8 of 10 Pro 🔒
Andrey SantosM 70%7 of 10 Pro 🔒
Trevoh ChalobahD 70%7 of 10 Pro 🔒
Enzo FernándezM 70%7 of 10 Pro 🔒
Pedro NetoM 60%6 of 10 Pro 🔒
Wesley FofanaD 60%6 of 10 Pro 🔒
Malo GustoD 60%6 of 10 Pro 🔒

Wolves
João GomesM 80%8 of 10 Pro 🔒
Yerson MosqueraD 70%7 of 10 Pro 🔒
Chelsea
Moisés CaicedoM 90%9 of 10 Pro 🔒

Wolves
João GomesM 70%7 of 10 Pro 🔒
Chelsea
No Chelsea player in this window.

Wolves
Yerson MosqueraD 60%6 of 10 Pro 🔒
Chelsea
Moisés CaicedoM 70%7 of 10 Pro 🔒

Wolves
No Wolves player in this window.
Chelsea
João PedroF 50%5 of 10 Stats only
Enzo FernándezM 40%4 of 10 Stats only
Cole PalmerM 30%3 of 10 Stats only

Wolves
No Wolves player in this window.
Chelsea
Robert SánchezG 80%8 of 10 Stats only

Wolves
No Wolves player in this window.
Chelsea
Robert SánchezG 60%6 of 10 Stats only

🔒 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
Jarred Gillett refereed this match 3.85cards a gameleague 4.08 80games
Jarred Gillett's card record

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.