Premier League player-market outlook — shots, fouls and the players who lead them
Not all player markets deserve your attention equally. This page covers the ones the model demonstrably beats — shots on target, total shots and fouls, where the public track record runs double digits ahead of the baseline — and maps who led them last season, how often their 1+ lines actually landed, and the short list of players who led more than one. It's form, not prophecy: these numbers describe what happened, and the model only prices a bet once there's a real opponent and a real price. But the season is long, profiles are sticky, and the punter who knows this map in August is reading a different game from the one who doesn't.
These are 25/26 Premier League rates for players with at least 10 starts, ordered by output per 90. Hit rate is how often the 1+ line would have landed in those starts. Teams shown are last season's; summer signings without a Premier League history appear once they play. This is descriptive form — the model prices each fixture against its actual opponent in-season, which is where the real edge is found.
On last season's numbers (25/26 Premier League), Erling Haaland (Man City) tops the shots-on-target watchlist — 1+ shot on target in 85% of starts, and Valentín Castellanos (West Ham) featured across 3 of the value markets. It's a pre-season map from completed matches, not a prediction.
Players who lead more than one value market
One column is a stat; two is a profile. A player who leads several value markets gives you more ways in — different lines, different prices, sometimes only one of them mispriced — and carries one warning worth knowing: his legs travel together. The match that delivers his shots tends to deliver his fouls, and bookmakers price that correlation into same-match combinations. The bet builder guide covers the trap.
| Player | Team | Leads these markets |
|---|---|---|
| Valentín Castellanos | West Ham | Shots on target, Total shots, Fouls committed |
| Benjamin Šeško | Man Utd | Shots on target, Total shots |
| Bukayo Saka | Arsenal | Total shots, Fouls won |
| Cole Palmer | Chelsea | Shots on target, Total shots |
| Emiliano Buendía | Aston Villa | Fouls committed, Fouls won |
| Erling Haaland | Man City | Shots on target, Total shots |
| Matheus Cunha | Man Utd | Shots on target, Total shots |
Shots on target — the leaders
Forwards own this list, as they should — but the column that separates a stat from a bet is the hit rate. A gaudy per-90 built on a few wild afternoons reads very differently from the same number spread evenly across thirty starts, and the 1+ line only pays the second kind. Prices here are short and the market's attention is heaviest, so the edge is in precision: how we model shots on target is the full walk.
| Player | Team | Per 90 | 1+ hit rate | Starts |
|---|---|---|---|---|
| Benjamin Šeško · forward | Man Utd | 1.87 | 67% | 15 |
| Erling Haaland · forward | Man City | 1.80 | 85% | 33 |
| Valentín Castellanos · forward | West Ham | 1.42 | 76% | 17 |
| Jean-Philippe Mateta · forward | Crystal Palace | 1.30 | 68% | 25 |
| Matheus Cunha · midfielder | Man Utd | 1.26 | 75% | 28 |
| Ollie Watkins · forward | Aston Villa | 1.20 | 61% | 33 |
| Richarlison · forward | Tottenham | 1.20 | 65% | 20 |
| Igor Thiago · forward | Brentford | 1.18 | 59% | 37 |
| Eli Junior Kroupi · midfielder | Bournemouth | 1.17 | 65% | 20 |
| Antoine Semenyo · midfielder | Man City | 1.15 | 74% | 35 |
| Cole Palmer · forward | Chelsea | 1.15 | 67% | 21 |
| Dominic Calvert-Lewin · forward | Leeds | 1.12 | 73% | 30 |
How SharpXI models shots on target →
Total shots — the leaders
The volume market — every attempt counts, so the ladder runs deeper (1+, 2+, 3+) and midfielders who shoot on sight climb a list that shots on target keeps them off. Notice how many leaders sit at or near a 100% hit rate on 1+: that line is close to a formality at the top, which is exactly why the interesting prices usually live further up the ladder, where the distribution's tail decides the bet.
| Player | Team | Per 90 | 1+ hit rate | Starts |
|---|---|---|---|---|
| Erling Haaland · forward | Man City | 3.83 | 100% | 33 |
| David Brooks · midfielder | Bournemouth | 3.54 | 83% | 12 |
| Benjamin Šeško · forward | Man Utd | 3.36 | 100% | 15 |
| Matheus Cunha · midfielder | Man Utd | 3.24 | 96% | 28 |
| Hugo Ekitiké · forward | Liverpool | 3.23 | 89% | 19 |
| Valentín Castellanos · forward | West Ham | 3.23 | 88% | 17 |
| Raúl Jiménez · forward | Fulham | 3.02 | 84% | 25 |
| Eberechi Eze · midfielder | Arsenal | 2.98 | 89% | 18 |
| Tolu Arokodare · forward | Wolves | 2.94 | 93% | 14 |
| Cole Palmer · forward | Chelsea | 2.93 | 95% | 21 |
| Bukayo Saka · forward | Arsenal | 2.87 | 100% | 23 |
| Mohamed Salah · midfielder | Liverpool | 2.85 | 91% | 23 |
How SharpXI models total shots →
Fouls committed — the leaders
The folklore says fouling belongs to centre-halves; the table says otherwise. The leaders are midfielders and pressing forwards — the players making challenges in the most contested third of the pitch — and the market, which spends its attention on goals and shots, prices these lines coarsely. That neglect is the whole reason fouls are among the model's strongest markets: every foul is two stats explains the machinery.
| Player | Team | Per 90 | 1+ hit rate | Starts |
|---|---|---|---|---|
| Saša Lukić · midfielder | Fulham | 2.75 | 100% | 17 |
| Zian Flemming · forward | Burnley | 2.49 | 90% | 20 |
| Amine Adli · forward | Bournemouth | 2.36 | 70% | 10 |
| Thierno Barry · forward | Everton | 2.19 | 74% | 19 |
| João Gomes · midfielder | Wolves | 2.18 | 90% | 31 |
| Joelinton · midfielder | Newcastle | 2.17 | 95% | 20 |
| Randal Kolo Muani · forward | Tottenham | 2.16 | 76% | 17 |
| Emiliano Buendía · midfielder | Aston Villa | 2.07 | 75% | 16 |
| Valentín Castellanos · forward | West Ham | 2.06 | 88% | 17 |
| Diego Gómez · midfielder | Brighton | 2.02 | 91% | 23 |
| Jørgen Strand Larsen · forward | Crystal Palace | 2.02 | 83% | 24 |
| Jhon Arias · midfielder | Wolves | 2.01 | 60% | 10 |
How SharpXI models fouls committed →
Fouls won — the leaders
The mirror image — the same collisions, credited to the other man. Dribblers and hold-up forwards spend their evenings being kicked, and it shows up here with the steadiest hit rates on the page. When a fouls-won leader meets a foul-prone opponent, both sides of the mirror are live at once — which is the match-up the model watches for.
| Player | Team | Per 90 | 1+ hit rate | Starts |
|---|---|---|---|---|
| Hannibal Mejbri · midfielder | Burnley | 3.38 | 100% | 13 |
| Jack Grealish · midfielder | Everton | 3.20 | 100% | 18 |
| Jérémy Doku · forward | Man City | 3.08 | 89% | 18 |
| Jean-Ricner Bellegarde · midfielder | Wolves | 2.95 | 92% | 12 |
| Patrick Dorgu · midfielder | Man Utd | 2.90 | 100% | 14 |
| Bruno Guimarães · midfielder | Newcastle | 2.75 | 85% | 27 |
| Crysencio Summerville · midfielder | West Ham | 2.43 | 93% | 28 |
| Mateus Mané · forward | Wolves | 2.35 | 83% | 18 |
| Emiliano Buendía · midfielder | Aston Villa | 2.27 | 88% | 16 |
| Xavi Simons · midfielder | Tottenham | 2.24 | 89% | 18 |
| Bukayo Saka · forward | Arsenal | 2.18 | 78% | 23 |
| Elliot Anderson · midfielder | Nottingham Forest | 2.16 | 89% | 37 |
How SharpXI models fouls won →
Frequently asked
Are these predictions for the new season?
No — they're last season's Premier League rates, shown for players with at least 10 starts. The model only prices a bet once there's a real fixture: an actual opponent, a projected lineup and a live price. Think of this page as the map you study before the season, not the bet slip.
What does hit rate mean here?
The share of a player's starts (60+ minutes) in which the 1+ line would have landed. It's the honesty column: a per-90 rate can flatter a player who piles stats into a few wild games, but a hit rate tells you how often the bet actually wins.
Why is there no cards market on this page?
Deliberately. Cards are the hardest thing we price — in every league we cover, at least one card line comes out no better than the base rate, and the Premier League track record says so — and this page leads with the markets the model demonstrably beats. The referee-driven card analysis lives in the modelling guides instead, where the caveats can travel with it.
Why do the fouls markets reward attention?
Because almost nobody gives them any. Sharp money concentrates on goals and shots, so foul lines are priced coarsely — and on the Premier League track record the 1+ fouls markets beat the baseline by double digits, among the model's strongest results.
Descriptive rates from public match data, shown for context. The model adds shrinkage, opponent adjustments and de-vigging before flagging value, and grades itself in public on the track record. 18+ — please gamble responsibly.