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Player-market outlook · 25/26

Bundesliga 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 Bundesliga 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 Bundesliga 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 Bundesliga), Harry Kane (FC Bayern München) tops the shots-on-target watchlist — 1+ shot on target in 92% of starts, and Can Uzun (Eintracht Frankfurt) 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: those legs travel together. The match that delivers the shots tends to deliver the fouls, and bookmakers price that correlation into same-match combinations. The bet builder guide covers the trap.

PlayerTeamLeads these markets
Can Uzun Eintracht Frankfurt Shots on target, Total shots, Fouls won
Christian Kofane Bayer 04 Leverkusen Shots on target, Total shots, Fouls committed
Michael Olise FC Bayern München Shots on target, Total shots, Fouls won
Deniz Undav VfB Stuttgart Shots on target, Total shots
Harry Kane FC Bayern München Shots on target, Total shots
Igor Matanović SC Freiburg Total shots, Fouls committed
Luis Díaz FC Bayern München Shots on target, Total shots
Patrik Schick Bayer 04 Leverkusen Shots on target, Total shots
Said El Mala 1. FC Köln Shots on target, Total shots
Serhou Guirassy Borussia Dortmund Shots on target, Total shots

Shots on target — the leaders

In the Bundesliga, Harry Kane (FC Bayern München) leads at 2.53 per 90, ahead of Michael Olise (1.94).

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.

PlayerTeamPer 90 1+ hit rateStarts
Harry Kane · forward FC Bayern München 2.53 92% 25
Michael Olise · midfielder FC Bayern München 1.94 91% 22
Deniz Undav · forward VfB Stuttgart 1.84 71% 24
Patrik Schick · forward Bayer 04 Leverkusen 1.53 65% 20
Serhou Guirassy · forward Borussia Dortmund 1.53 56% 27
Lennart Karl · midfielder FC Bayern München 1.47 50% 12
Can Uzun · midfielder Eintracht Frankfurt 1.46 77% 13
Said El Mala · forward 1. FC Köln 1.43 79% 19
Sheraldo Becker · forward 1. FSV Mainz 05 1.42 82% 11
Jonathan Burkardt · forward Eintracht Frankfurt 1.41 60% 15
Luis Díaz · midfielder FC Bayern München 1.36 80% 25
Christian Kofane · forward Bayer 04 Leverkusen 1.35 55% 11

How SharpXI models shots on target →

Total shots — the leaders

In the Bundesliga, Deniz Undav (VfB Stuttgart) leads at 4.89 per 90, ahead of Harry Kane (4.50).

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.

PlayerTeamPer 90 1+ hit rateStarts
Deniz Undav · forward VfB Stuttgart 4.89 100% 24
Harry Kane · forward FC Bayern München 4.50 96% 25
Michael Olise · midfielder FC Bayern München 3.92 95% 22
Serge Gnabry · forward FC Bayern München 3.66 100% 13
Said El Mala · forward 1. FC Köln 3.45 95% 19
Christian Kofane · forward Bayer 04 Leverkusen 3.26 82% 11
Patrik Schick · forward Bayer 04 Leverkusen 3.25 80% 20
Luis Díaz · midfielder FC Bayern München 3.23 100% 25
Serhou Guirassy · forward Borussia Dortmund 3.22 89% 27
Can Uzun · midfielder Eintracht Frankfurt 3.15 92% 13
Alexis Claude-Maurice · midfielder FC Augsburg 3.03 96% 24
Igor Matanović · forward SC Freiburg 2.98 86% 14

How SharpXI models total shots →

Fouls committed — the leaders

In the Bundesliga, Tim Lemperle (TSG Hoffenheim) leads at 2.76 per 90, ahead of Igor Matanović (2.46).

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.

PlayerTeamPer 90 1+ hit rateStarts
Tim Lemperle · forward TSG Hoffenheim 2.76 91% 22
Igor Matanović · forward SC Freiburg 2.46 86% 14
Ermedin Demirović · forward VfB Stuttgart 2.21 75% 16
Oscar Højlund · midfielder Eintracht Frankfurt 2.21 82% 11
Christian Kofane · forward Bayer 04 Leverkusen 2.13 82% 11
Wouter Burger · midfielder TSG Hoffenheim 2.10 82% 28
Christoph Baumgartner · midfielder RB Leipzig 2.03 91% 32
Marvin Pieringer · forward 1. FC Heidenheim 2.01 81% 16
Lovro Majer · midfielder VfL Wolfsburg 2.01 77% 13
András Schäfer · midfielder 1. FC Union Berlin 2.00 77% 13
Vinícius Souza · midfielder VfL Wolfsburg 1.99 90% 20
Phillip Tietz · forward 1. FSV Mainz 05 1.89 95% 19

How SharpXI models fouls committed →

Fouls won — the leaders

In the Bundesliga, Johan Manzambi (SC Freiburg) leads at 3.35 per 90, ahead of Antonio Nusa (2.83).

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.

PlayerTeamPer 90 1+ hit rateStarts
Johan Manzambi · midfielder SC Freiburg 3.35 96% 24
Antonio Nusa · midfielder RB Leipzig 2.83 78% 23
Derry Scherhant · midfielder SC Freiburg 2.48 75% 12
Michael Olise · midfielder FC Bayern München 2.25 91% 22
Ezequiel Fernández · midfielder Bayer 04 Leverkusen 2.16 80% 10
Mattias Svanberg · midfielder VfL Wolfsburg 2.10 90% 10
Nicolás Capaldo · midfielder Hamburger SV 2.06 86% 22
Assan Ouédraogo · midfielder RB Leipzig 2.03 100% 11
Ilyas Ansah · midfielder 1. FC Union Berlin 2.03 77% 22
Can Uzun · midfielder Eintracht Frankfurt 2.00 77% 13
Kevin Stöger · midfielder Borussia M'gladbach 1.95 85% 13
Benedikt Gimber · defender 1. FC Heidenheim 1.93 85% 13

How SharpXI models fouls won →

Frequently asked

Are these predictions for the new season?

No — they're last season's Bundesliga 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 Bundesliga 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 Bundesliga 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.