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Team reportBundesliga 2026

Mainz

A possession-dominant side, aggressive high-press out of possession, with a high-volume attack. Currently #7 of 18 on 10 points (3-1-3, 16:13). Tactical profile below is built from 7 matches of ball-by-ball event data, contextualised against the league.

possession-dominantaggressive high-presshigh-volume attack
W
W
L
D
L
W
L
W
D
W
L
W
L
W
L
10
points
#7 of 18 · 7 games
16:13
goals
+3 GD
3-1-3
record
W-D-L
1.43
points/game
#7 in the league
2.09
xG for/game
rank #4
1.61
xG against/game
rank #10
#8
press rank
of 18 · most aggressive
60%
field tilt
#3
Mainz
League average
Expected goals for / game
2.091.63
Expected goals against / game
1.611.63
Field tilt
60%50%
Pass retention
79%79%
Overview

The team

Who they are and how the season went.

Who Are They? — Identity

The question
In one read, what kind of team is Mainz and how good was the season?
Read. Mainz currently sit #7 of 18 on 10 points (3W-1D-3L, 1.43 pts/game), scoring 16 and conceding 13 for a goal difference of +3. On the ball they are a possession-dominant team (60% field tilt, #3 in the league); off it they defend with a aggressive high-press (#8 of 18 for press intensity). Across the 7 matches of event data, they create 2.09 xG/game (#4) and concede 1.61 (#10), a net of +0.48 xG/game — the underlying performance matches the actual goal difference.

Season Context — the league table

The question
Where did Mainz actually finish, and against whom?

The authoritative final table (full season), centred on Mainz.

#teamPWDLGFGAGDPts
4Leverkusen74121612+413
5RBL74032011+912
6Werder Bremen73311514+112
7Mainz73131613+310
8Elversberg73131215-310
9Augsburg72321713+49
10Hoffenheim73041414+09

Match Momentum — when they create and concede danger

The question
Across a whole game, which periods does Mainz actually dominate for chances — and which periods hurt them?

Net xG (created minus conceded) in each 15-minute window, summed across the whole season's matches — above the line is time they dominate for chances, below is time it hurts them. Real on-clock shot timing.

dominant (net xG for)conceding (net xG against)
even0-15'16-30'31-45'46-60'61-75'76-90+'+3.49-1.37
Vision AI Model Insight
Mainz tend to fade after a strong start — first-half net xG +3.53, second-half -0.18. Their strongest period is 31-45' (net +3.49 xG); their most dangerous period to face is 61-75' (net -1.37 xG against them).

xG Race — the season, match by match

Expected goals piling up over 7 matches: Mainz against what their opponents created. A line pulling away upwards is a side creating more than it gives away; the gap at the end is the season’s balance (+3.4 xG). Hover a point for that match.

05111614.611.3Paderborn (H) 0-0 · xG 1.25 to 0.89Hamburg (A) 5-0 · xG 2.54 to 0.30Eintracht Frankfurt (H) 1-3 · xG 2.42 to 1.65Borussia Monchengladbach (A) 4-3 · xG 2.63 to 3.42Leverkusen (H) 3-4 · xG 2.88 to 1.67Schalke (A) 1-0 · xG 1.36 to 0.48Werder Bremen (H) 2-3 · xG 1.52 to 2.86M1M2M3M4M5M6M7
MainzOpponents against Mainz

Tactical DNA

The football question
In five genes, what is Mainz's footballing identity?

Each gene is a 0–100 score — the team's league percentile across the metrics that define that phase of play. It distils the whole report into an identity fingerprint: the taller the gene, the more that trait defines them.

67
Build-Up
possession control from the back
83
Progression
moving the ball upfield with threat
76
Creation
manufacturing chances
69
Press
winning the ball high & hard
44
Solidity
resisting the opponent's threat
Genetic identity
Mainz are defined most by their Progression DNA (83/100 · strong at moving the ball upfield with threat), and least by their Solidity DNA (44/100). Percentiles vs the 18 teams in the league.

The Playstyle Wheel

Fifteen style metrics across five domains — Possess, Disrupt, Finish, Press, Defend — each a percentile against the rest of the league. Metrics marked ~proxy use the closest available event-data signal where the standard definition needs data this feed doesn't carry (explicit pressure events, offside/keeper-sweep incidents).

Possess38Disrupt41Finish71Press57Defend57
Possess
Deep buildup
10%
Possession
38%
Field tilt
67%
Disrupt
Central progression ~proxy
19%
Dribble tendency
29%
Progressive passes
76%
Finish
Final-third patience
71%
Shot quality
76%
Open-play xG
67%
Press
Forward press ~proxy
52%
High regains
52%
Pressing efficiency ~proxy
67%
Defend
Def-mid press ~proxy
38%
High line ~proxy
76%
PAdj defensive actions
57%
Mainz is defined most by Finish (71% in the league) and weakest on Possess (38%). Percentiles vs the 21 teams in the league this season.
In possession

In possession

How they keep the ball, build up, progress, create and finish, and where their attacks die.

Keeping the ball6 sections

Field Tilt — Territorial Control

The question
How much of the pitch does Mainz actually control, relative to the rest of the league?

Field tilt = share of final-third touches between the two sides across Mainz's matches, the standard proxy for territorial dominance. Plotted against points per game for every side in the league — the real question isn't just who controls territory, it's whether that control actually converts into results. Dashed lines mark the league average on each axis; Mainz highlighted. Hover any dot for the team.

Borussia Dortmund: 57% tilt, 2.43 pts/gBayern: 77% tilt, 2.43 pts/gFreiburg: 55% tilt, 1.86 pts/gLeverkusen: 57% tilt, 1.86 pts/gWerder Bremen: 42% tilt, 1.71 pts/gRBL: 54% tilt, 1.71 pts/gMainz: 60% tilt, 1.43 pts/gElversberg: 37% tilt, 1.43 pts/gHamburg: 35% tilt, 1.29 pts/gAugsburg: 44% tilt, 1.29 pts/gHoffenheim: 62% tilt, 1.29 pts/gPaderborn: 46% tilt, 1.14 pts/gEintracht Frankfurt: 48% tilt, 1.14 pts/gKoln: 48% tilt, 1.14 pts/gUnion Berlin: 41% tilt, 0.86 pts/gStuttgart: 56% tilt, 0.86 pts/gSchalke: 38% tilt, 0.86 pts/gBorussia Monchengladbach: 44% tilt, 0.43 pts/gMainz35%49%63%77%0.41.11.82.4FIELD TILT %POINTS / GAME
Vision AI Model Insight
Mainz post 60% field tilt, ranking #3 of 18 in the league — a genuinely territory-dominant side, and their 1.43 points/game sits above the league-average return — territory is genuinely converting into results.

Where the time goes — how long each phase lasts

The question
When Mainz has the ball, how much of that time is spent building, progressing and creating?

The share of Mainz's time on the ball spent in each phase of play, in the order the phases happen. A phase is where the ball is on the pitch (own third, middle third, final third); a transition or a set piece skips the build-up and is counted on its own.

Build-up: 16.2% of the time on the ball16%Progression: 47.8% of the time on the ball48%Chance creation: 33.2% of the time on the ball33%Attacking transition: 2.7% of the time on the ball48%PROGRESSION
PhaseShare of the time
Build-up16.2%
Progression47.8%
Chance creation33.2%
Attacking transition2.7%
Vision AI Model Insight
Progression is where Mainz spends most of its time on the ball (48% of its time on the ball).

Passing Network

The question
Who actually plays with whom — the real on-pitch connections, not just average positions?

Every pass linked to its likely receiver (the next same-team touch), aggregated into player-pair combinations at each player's average position. Line width = how often the pair combines; colour = threat generated per pass (blue low → red high). Hover a line for the pair, or click a player to isolate their connections.

Phillipp Mwene ↔ Kacper Potulski: 95 passes, -0.1 combined xTStefan Posch ↔ Fabio Gruber: 83 passes, -0.2 combined xTNadiem Amiri ↔ Stefan Posch: 75 passes, 0.2 combined xTStefan Posch ↔ Kaishu Sano: 75 passes, -0.3 combined xTStefan Posch ↔ Kacper Potulski: 72 passes, 0.3 combined xTNadiem Amiri ↔ Kaishu Sano: 67 passes, 0.9 combined xTFabio Gruber ↔ Kacper Potulski: 66 passes, -0.1 combined xTNadiem Amiri ↔ Anthony Caci: 64 passes, 2.2 combined xTNadiem Amiri ↔ Kacper Potulski: 64 passes, 1.3 combined xTKaishu Sano ↔ Kacper Potulski: 58 passes, 0.4 combined xTAnthony Caci ↔ Stefan Posch: 55 passes, -0.3 combined xTNadiem Amiri ↔ Fabio Gruber: 54 passes, -0.9 combined xTDanny da Costa ↔ Anthony Caci: 50 passes, 0.6 combined xTDanny da Costa ↔ Stefan Posch: 49 passes, 0.1 combined xTAnthony Caci ↔ Kaishu Sano: 47 passes, 0.3 combined xTSheraldo Becker ↔ Anthony Caci: 46 passes, 1.5 combined xTAlexander Schwolow ↔ Kacper Potulski: 40 passes, -0.4 combined xTPhillipp Mwene ↔ Lee Jae-Sung: 37 passes, 0.2 combined xTPhillipp Mwene ↔ Nadiem Amiri: 35 passes, 0.6 combined xTAlexander Schwolow ↔ Fabio Gruber: 34 passes, 0.0 combined xTLee Jae-Sung ↔ Kaishu Sano: 34 passes, 0.4 combined xTNadiem Amiri ↔ Phillip Tietz: 34 passes, 1.9 combined xTAlexander Schwolow ↔ Stefan Posch: 33 passes, 0.0 combined xTSheraldo Becker ↔ Nadiem Amiri: 33 passes, 2.0 combined xTDanny da Costa — isolate this player's connectionsCostaPhillipp Mwene — isolate this player's connectionsMweneSheraldo Becker — isolate this player's connectionsBeckerLee Jae-Sung — isolate this player's connectionsJae-SungNadiem Amiri — isolate this player's connectionsAmiriPhillip Tietz — isolate this player's connectionsTietzAnthony Caci — isolate this player's connectionsCaciRansford Königsdörffer — isolate this player's connectionsKönigsdörfKaishu Sano — isolate this player's connectionsSanoKacper Potulski — isolate this player's connectionsPotulskiStefan Posch — isolate this player's connectionsPoschAlexander Schwolow — isolate this player's connectionsSchwolowFabio Gruber — isolate this player's connectionsGruberEric Martel — isolate this player's connectionsMartelBenedict Hollerbach — isolate this player's connectionsHollerbachPaul Nebel — isolate this player's connectionsNebel
width = passes between the paircolour = xT per pass: low → high

Top 10 combinations

Mwene ↔ Potulski
95p · -0.1xT
Posch ↔ Gruber
83p · -0.2xT
Amiri ↔ Posch
75p · 0.2xT
Posch ↔ Sano
75p · -0.3xT
Posch ↔ Potulski
72p · 0.3xT
Amiri ↔ Sano
67p · 0.9xT
Gruber ↔ Potulski
66p · -0.1xT
Amiri ↔ Caci
64p · 2.2xT
Amiri ↔ Potulski
64p · 1.3xT
Sano ↔ Potulski
58p · 0.4xT
Vision AI Model Insight
Busiest connection: Phillipp Mwene ↔ Kacper Potulski (95 passes). Most dangerous per pass: Sheraldo Becker ↔ Nadiem Amiri (0.06 xT/pass over 33 passes) — the combination that hurts opponents most when it fires, not just the one used most.

Touch Density — where Mainz had the ball

Every touch across 63 matches. Brighter means more touches.

Which Zones Do They Attack Through?

The question
Across the whole pitch, where does Mainz actually do its on-ball work?

Passes on one map, carries (the ball travelling with a player) on the other. Each is binned into the 15 pitch zones (three thirds × five vertical lanes) and shaded by its share of that kind of action; the arrows are the real actions.

Passes
the ball moved by a pass
3557
ACTIONS
3%4%9%3%2%9%11%13%11%7%8%5%4%4%6%PASSES
Carries
the ball moved with a player
660
ACTIONS
2%5%13%3%6%10%18%11%8%5%4%2%5%6%CARRIES
Vision AI Model Insight
Their single busiest zone is the centre of the middle third (14.2% of all actions). 66% of actions run through the inside lanes (centre + both half-spaces) versus 34% down the two wings — an inside-dominant, half-space build. By third: 22% defensive, 51% middle, 27% attacking.

Attack Sides

Which channel they attacked through this season: the share of every action made in the opponent’s half, down the left, through the middle and down the right. Mainz leaned on their right (39%). The bars under the pitch put the league’s share beside theirs.

33%Left side28%Middle39%Right sideATTACKING →
Mainz
33%28%39%
League
39%25%36%

Final-Third Entries

Every completed pass or carry that took the ball from outside the final third into it, by the channel it arrived in. Mainz made 347 this season (49.6 a match); 29% came into the right wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.

Left wing: 70 entries (20%)7020%LEFT WINGLeft half-space: 56 entries (16%)5616%LEFT HALF-SPACECentre: 54 entries (16%)5416%CENTRERight half-space: 65 entries (19%)6519%RIGHT HALF-SPACERight wing: 102 entries (29%)10229%RIGHT WINGATTACKING ↑
ChannelEntriesPer matchShareLeague share
Left wing7010.020%28%
Left half-space568.016%17%
Centre547.716%14%
Right half-space659.319%15%
Right wing10214.629%26%
Build-up2 sections

Build-Up — the pass map

The football question
How does Mainz progress possession from defence into attack — and who runs it?

Build-up passes grouped into 5 real categories — not the old zone grid, not raw individual lines: CB (passer is a centre-back), Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top passer.

765build-up passes attempted
77%completed
32%reach the middle third
3%go long into the final third

Pass types

270CB · 46%
30Long Wide · 5%
19Long Central · 3%
185Wide → FB · 32%
82Central · 14%

Sequence success — build-up

90build-up sequences this season
6%succeed — reach the next third or a shot
34%turned over with nothing to show
CB270 · 46%
left: 137 passes, most from Potulski137right: 133 passes, most from Gruber133
Long Wide30 · 5%
left: 22 passes, most from Schwolow22right: 8 passes, most from Schwolow8
Long Central19 · 3%
left: 12 passes, most from Zentner12right: 7 passes, most from Schwolow7
Wide → FB185 · 32%
right: 113 passes, most from Schwolow113left: 72 passes, most from Schwolow72
Central82 · 14%
left: 41 passes, most from Schwolow41right: 41 passes, most from Schwolow41
◆ Vision AI Insight
Mainz build predominantly down the right, with Schwolow as the fulcrum (107 build-up passes). Only 35% of their build-up passes actually advance a third — the rest is lateral circulation, the hallmark of a possession-controlling side that circulates patiently in midfield before progressing. Their single most-repeated pattern is C def → RHS def (51×).

Pass Explorer

Every pass of Mainz’s last 7 matches as an arrow, theirs first and the opponents’ with other team, each side attacking left to right (solid = completed, dashed grey = failed). Eight families per team — click any map to open it wide, then filter by channel or third, from one to another (or click a cell of the flow grids), by outcome, half, minute and passer. Channels: right wing, right half-space, centre, left half-space, left wing.

Progression4 sections

How They Progress — the mechanism

Not just how many progressive passes, but where they start, who plays them, and whether they actually penetrate. 418 progressive passes across 7 matches (59.7/game).

Origin lane

Right wing
34%
Right half-space
18%
Central
15%
Left half-space
11%
Left wing
22%
Progression is right-dominant: 52% right side · 15% central · 33% left.

Who generates it

Nadiem Amiri
85 · 20%
Anthony Caci
57 · 14%
Stefan Posch
41 · 10%
Phillipp Mwene
37 · 9%
Kaishu Sano
28 · 7%
Top two — Nadiem Amiri & Anthony Caci — account for 34% of all progression.
PROGRESSION
59.7 prog passes/game
mostly right
→
PENETRATION
31% reach final third
25% reach the box
→
THREAT
the other 69% is recycled
progress ≠ danger
What came of it. 31% of progressive passes reached the final third and 25% the box; the rest was recycled in front of the block. 34% of the progression ran through two players, mostly down the right side.

Which lanes actually create the threat

Share of passes played from each lane against its share of the threat (xT) they add. Efficient = more threat than volume, sterile = lots of the ball, little damage.

Per lane — volume vs threat
Right wingefficient
Right half-spacebalanced
Centralsterile
Left half-spacesterile
Left wingbalanced

Progression — the pass map

The football question
Once Mainz have the ball in midfield, how do they actually break into the final third — and who carries that job?

Progression passes grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top passer.

1810progression passes attempted
85%completed
19%break into the final third
9%go backward into the defensive third

Pass types

609CB · 39%
50Long Wide · 3%
14Long Central · 1%
659Wide → FB · 43%
214Central · 14%

Sequence success — progression

159progression sequences this season
26%succeed — reach the next third or a shot
45%turned over with nothing to show
CB609 · 39%
left: 306 passes, most from Potulski306right: 303 passes, most from Posch303
Long Wide50 · 3%
left: 28 passes, most from Mwene28right: 22 passes, most from Caci22
Long Central14 · 1%
right: 9 passes, most from Caci9left: 5 passes, most from Caci5
Wide → FB659 · 43%
right: 365 passes, most from Amiri365left: 294 passes, most from Mwene294
Central214 · 14%
right: 111 passes, most from Amiri111left: 103 passes, most from Amiri103
◆ Vision AI Insight
Mainz progress predominantly down the right, with Posch the busiest midfield outlet (224 progression passes). Only 19% of passes that start in midfield actually break the final-third line — the rest (72%) is recycled back into midfield or (9%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is RHS mid → RW mid (95×) — press that lane and midfield progression stalls.

Decision Quality — not all completed passes are equal

A 90% pass completion can hide a sideways habit. Grading every pass by the threat it adds separates value creation from safe circulation.

Excellent (high xT gain)
17%
Progressive
1%
Neutral / sideways
47%
Safe / backward
34%
18% of passes are progressive-or-better; the remaining 82% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.

Carries Map — progressive ball-carries, squad-wide

The question
Where does Mainz actually progress the ball with their feet, not a pass?

153 progressive carries this season across the whole squad (23% of 660 tracked carries total), grouped into up to 4 spatial patterns by where each carry starts and ends together — not just its origin zone. Median 15m, longest 52m. Most active carrier: Gruber (29). Filter to one player below.

First (48)Second (45)Third (34)Fourth (26)grouped by carry pattern (start + end position), largest group first
startendown goal left, attacking →
Chance creation4 sections

Chance creation — how the shots come

The question
Where do Mainz's chances actually come from?

Every shot sorted twice. First by the kind of attack: an attacking transition (the shot came within 15 seconds of winning the ball), a settled attack (open play after the team had the ball), or a set piece. Then by how it was set up: a cross, a cut-back, a long ball, a pass into the box, or no pass at all (the player created it himself). So a cut-back from an attacking transition is counted apart from a cut-back in a settled attack.

124shots
14.6xG
0.12xG per shot
9big chances (0.3+ xG)
16goals
#2key passes / game in the league
#5box entries / game in the league
Attacking transition: 25 shots (20%)20%25Settled attack: 75 shots (60%)60%75Set piece: 24 shots (19%)19%24124SHOTS
Kind of attack, then how the shot was set upShotsGoalsxGxG / shot
Attacking transition 20% of shots2512.910.12
Cross500.410.08
Cut-back100.140.14
Long ball100.070.07
Pass into the box / short pass1211.520.13
Own creation (carry, dribble, rebound)600.770.13
Settled attack 60% of shots75128.430.11
Cross3163.110.10
Cut-back610.550.09
Long ball400.250.06
Pass into the box / short pass2142.870.14
Own creation (carry, dribble, rebound)1311.650.13
Set piece 19% of shots2433.280.14
Corner2232.460.11
Free kick100.030.03
Penalty100.790.79

Every attempt to create — and what came of it

Every pass into the box, every cut-back and every pass before a shot. Red led to a shot, gold to a goal; grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball. Filter by outcome, type or player; tap an arrow for the detail.

Who sets the shots up

PlayerSet-upsxG createdGoals
Sheraldo Becker162.053
Nadiem Amiri121.622
Lee Jae-Sung111.281
Anthony Caci111.023
Kaishu Sano90.971

Who takes them

PlayerShotsxGGoals
Phillip Tietz293.955
Sheraldo Becker182.082
Nadiem Amiri131.660
Anthony Caci81.413
Phillipp Mwene91.361
Vision AI Model Insight
The biggest source of Mainz's chances, by xG, is settled attack (8.4 of 14.6 xG, 75 of 124 shots), mostly cross. 25 of the 124 shots (20%) came within 15 seconds of winning the ball. 24 of the 124 shots came from set pieces; 16 goals from 14.6 xG (+1.4).

Chance Creation — the pass map

The football question
How does Mainz consistently create dangerous chances — from which zones, and who creates them?

Key passes (the pass immediately before a shot) grouped into 5 real categories: CB, Wide→FB, Central, Long Wide, Long Central. Each gets its own panel below, so nothing competes for attention. Arrow width = volume, labelled with the exact count; hover for the top creator.

Pass types

3CB · 3%
5Long Wide · 5%
17Long Central · 18%
18Wide → FB · 19%
50Central · 54%

Sequence success — final-third

146final-third sequences this season
23%succeed — produce a shot
40%turned over with nothing to show

Danger Funnel — which service carries threat

Every key pass pooled by where it was played FROM, then drawn with its arrow width set by total xT rather than by count. A zone that produces a lot of cheap balls shrinks; one that produces a few decisive ones dominates. Where this disagrees with the volume map, the volume map is the misleading one.

0 key passes shown● low threat● mid● high threatarrow width = total xT, not count
CB3 · 3%
right: 3 passes, most from Posch3
Long Wide5 · 5%
right: 3 passes, most from Mwene3left: 2 passes, most from Schwolow2
Long Central17 · 18%
left: 9 passes, most from Amiri9right: 8 passes, most from Amiri8
Wide → FB18 · 19%
left: 12 passes, most from Amiri12right: 6 passes, most from Amiri6
Central50 · 54%
left: 25 passes, most from Amiri25right: 25 passes, most from Caci25

Who creates — volume × threat

23Amiri
15Becker
14Caci
10Sano
9Jae-Sung
5Tietz
5Mwene
93key passes #2 of 18
18.6box entries/game #5 of 18
44.0final-3rd entries/game #5 of 18
2.09xG/game #4 of 18
16goals #4 of 18
+1.4finishing vs xG #9 of 18
◆ Vision AI Insight
Mainz lean right — 47% of their key passes come from that flank and its half-space (44 of 93), most often from Caci (13). They funnel chances into the c of the final third, most often via RW att → C att (21 key passes, most often from Caci). Amiri is the primary source overall. They generate 2.09 xG a game and finish it clinical — they beat their xG, led by Phillip Tietz (5G), Anthony Caci (3G), Sheraldo Becker (2G).

Crosses & Cutbacks — squad-wide

The question
How does Mainz deliver into the box from wide areas, and what comes of it?

149 crosses: 62 found a team-mate (42%), 35 led to a shot (23%). 36 cut-backs: 17 found a team-mate (47%), 9 led to a shot (25%). A cross starts wide and is aimed into the box; a cut-back starts near the byline and is squared back. Grey ones were tried but no shot came: solid found a team-mate, dashed lost the ball.

Crosses by Territory

The question
Which part of the final third does Mainz actually cross from?

149 crosses this season (the same crosses as the map above), split into fifteen territories — five channels (both wings, both half-spaces and the centre) by three depths from the halfway line to the byline. 32% come from one territory, the advanced right wing; 4 come from the half-spaces and 0 from the centre.

deep right wing: 7 crosses (5%)5%7deep right half-space: 1 crosses (1%)1%1deep centre: 0 crosses (0%)deep left half-space: 0 crosses (0%)deep left wing: 3 crosses (2%)2%3advanced right wing: 47 crosses (32%)32%47advanced right half-space: 1 crosses (1%)1%1advanced centre: 0 crosses (0%)advanced left half-space: 1 crosses (1%)1%1advanced left wing: 14 crosses (9%)9%14byline right wing: 43 crosses (29%)29%43byline right half-space: 0 crosses (0%)byline centre: 0 crosses (0%)byline left half-space: 1 crosses (1%)1%1byline left wing: 31 crosses (21%)21%31
TerritoryCrossesShareLed to a shot
advanced · right wing4732%8
byline · right wing4329%10
byline · left wing3121%9
advanced · left wing149%5
deep · right wing75%1
deep · left wing32%0
deep · right half-space11%1
advanced · right half-space11%1
advanced · left half-space11%0
byline · left half-space11%0
Finishing3 sections

Shot Map

124 shots for 14.6 xG (2.09/game, #4 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.

High-volume attack. 17.7 shots/game at 0.118 xG/shot. Box entries 19/game (#5). Threat created 14.0 xT/game (#3).

Goal Sequences

The move behind each of Mainz’s 16 goals this season, from the moment they got the ball to the finish. Pick a goal from the list, then step through it or press Play: numbered circles are passes and touches, dotted lines are carries (the ball travelling with a player), dashed grey is a pass that was lost, the star is the shot.

Each goal’s possession, rebuilt from the raw event feed — 16 goals traced.

How Do They Score?

The question
Where do the goals actually come from — open play, dead balls, which foot?

Every goal Mainz scored across the loaded matches, broken down by how it was created and finished.

By situation

Open play
13 · 81%
Set pieces
3 · 19%
Penalties
0 · 0%

By finish

Right foot
6 · 38%
Left foot
6 · 38%
Header
4 · 25%
Vision AI Model Insight
16 goals scored. 81% come in open play; set pieces add 19%. Open play is clearly their main route to goal.
When they lose it1 section

Ball-Loss Map — where attacks die

Every turnover has a location and a cause. This is a rate, not a raw count — the share of pass attempts from each zone that end in a turnover, so a zone they simply play through more often doesn't automatically look riskier. Grey cells are real zones with too few attempts to trust a rate.

Loss rate by zone

40 of 103 pass attempts lost here (39%).39%40/103Caci26 of 139 pass attempts lost here (19%).19%26/139Schwolow75 of 334 pass attempts lost here (22%).22%75/334Schwolow19 of 120 pass attempts lost here (16%).16%19/120Potulski19 of 69 pass attempts lost here (28%).28%19/69Mwene60 of 306 pass attempts lost here (20%).20%60/306Caci62 of 393 pass attempts lost here (16%).16%62/393Gruber58 of 480 pass attempts lost here (12%).12%58/480Tietz44 of 375 pass attempts lost here (12%).12%44/375Tietz40 of 256 pass attempts lost here (16%).16%40/256Mwene96 of 293 pass attempts lost here (33%).33%96/293Caci50 of 169 pass attempts lost here (30%).30%50/169Becker37 of 151 pass attempts lost here (25%).25%37/151Tietz49 of 149 pass attempts lost here (33%).33%49/149Mwene59 of 220 pass attempts lost here (27%).27%59/220Mweneattacking →

How they lose it

Incomplete pass — attacking third
291
Failed forward pass
206
Failed take-on
60
Lost in the defensive third
179
Their weakest zone is the right wing in the defensive third: 39% of 103 pass attempts there are lost (40 times). Separately, 40% of all losses happen in the attacking third — a fair share happen deeper, which invites transitions.
Out of possession

Out of possession

How they press, where they win the ball back, and how they concede.

Pressing2 sections

Pressing — the full picture

The football question
How hard does Mainz press, where, and does it actually work — beyond a single PPDA number?

A composite Pressing Index built from four event-derived dimensions, each ranked against the league. Not one metric — intensity, height, effectiveness and opponent disruption together. The pitch shows every defensive action (the only five events this data records); the white line is the average height that feeds the score. Toggle by action type below, hover any dot for the player.

68
Pressing Index
/100 · strong
Intensity67
PPDA, high-press share, high-zone actions
Height78
how high up the pitch they defend
Effectiveness58
high recoveries & high-regain share
Disruption72
opponent completion & progression allowed

Every defensive action & where it happens

Where they win the ball back — by lane

RW
15%
RHS
25%
C
21%
LHS
23%
LW
16%
By third (high / mid / low regain)
Def 45%
Mid 39%
Att 16%
Read
Ranked #8 of 18 for press intensity, this is a high press. 55% of defensive actions happen in the middle & attacking thirds, at an average height of 40. Of recoveries, 56% are won high up the pitch.

Every pressing metric, ranked in the league

metricvaluerankmeaning
PPDA (opp passes per def action)—#8/18lower = more intense; rank is what matters
Defensive actions / game170#9/18
High-press share24%#4/18def actions won high up
Defensive-action height (0–100)40.0#4/18avg pitch height of actions
High recoveries / game23.0#8/18ball won in mid+att third
High-recovery share56%#6/18share of recoveries won high
Opponent completion allowed79%#6/18lower = more disruptive
Opponent progressive allowed8.6%#4/18of opp passes, lower = better
Transition timing
This source carries per-event timestamps, so genuine transition metrics — counter-attack shots, press→shot conversions, time-to-recover — are measured; see the dedicated Transitions section below.

How long they keep it — passes and seconds until the ball is won

The question
Once the opponent has the ball, how many passes and how many seconds pass before Mainz win it back with a defensive action?

Counted over the 227 opposition possessions that Mainz ended with a tackle, interception, recovery or challenge. Fewer passes and seconds means the ball is won quickly (a press); more means the opponent is allowed to play before Mainz step in. Mainz win it after 5.5 opponent passes in the first half-hour and after 3.8 after the hour mark (sooner).

PASSES THEY GET
4.8passes
median 3 · before Mainz win it
TIME THEY KEEP IT
14sec
median 6s · 32.4 wins per game
How many passes the opponent got in before the ball was won
48%
25%
14%
13%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.9
18s
15–30′
4.9
13s
30–45+′
4.6
11s
45–60′
5.7
17s
60–75′
4.3
11s
75–90+′
3.4
11s
average passesaverage seconds

Attitude in each third

Every other style number here says how much Mainz does. This says how: wide or central, short or long, patient or direct. Bars are standard deviations from the league mean for passes played from that third; a solid bar marks a dimension where Mainz are genuinely unusual, and a verdict is only written where something clears 0.85 sd — a side that is ordinary in a third is told so rather than given a label for average play.

Build-up (own third) 765 passes

narrow, central, played inside

Width of use-1.4018.11
Wide-channel share-1.110.24
Side bias-0.81-2.88
Pass length+0.1323.17
Long-ball share+0.230.25
Forward gain per pass+0.3713.05
Backward-pass share-0.480.18
Deep-pass share+0.420.33
Completion-0.250.77
Seconds between passes-0.073.29
Side used

left 20% · central 52% · right 28%

Progression (middle third) 1,810 passes

narrow, central, played inside

Width of use-1.2622.90
Wide-channel share-1.050.33
Side bias-0.37-1.39
Pass length+0.1817.47
Long-ball share-0.070.10
Forward gain per pass+0.414.23
Backward-pass share-0.750.30
Deep-pass share+0.360.18
Completion+0.110.85
Seconds between passes-0.123.14
Side used

left 30% · central 35% · right 35%

Final third 982 passes

nothing unusual in this zone

Width of use-0.6729.96
Wide-channel share-0.550.54
Side bias-0.40-3.31
Pass length+0.1115.63
Long-ball share+0.140.11
Forward gain per pass+0.071.13
Backward-pass share-0.070.38
Deep-pass share-0.020.09
Completion+0.060.70
Seconds between passes+0.233.57
Side used

left 35% · central 20% · right 45%

Reference measured across roughly 3,500 match-teams in this competition, per third, so the comparison is with Mainz's own league. The means behave as football should: width of use grows 21.0 → 25.6 → 31.6 going forward, pass length shrinks 22.7 → 17.2 → 15.5 m, and completion is hardest in the final third. Tempo is integer-resolution because the feed records whole seconds.

Transitions1 section

Transitions — attacking & defensive

The question
When Mainz win or lose the ball, what actually happens in the seconds after?

Reconstructed from the full timestamped event stream: real possession spells (not zone-clustered), and shots that follow a regain within a fixed window. This phase of play needs ordered, per-event timestamps — which every match here carries.

Direct play share20%
#2 of 18 · reaches final third in ≤3 passes
Patient build-up share21%
#15 of 18 · spells of 6+ passes
Tempo (possession length)15.0s
#4 of 18 · shorter = faster tempo
Passes per possession3.8
#15 of 18
Counter-attack shots /game0.85
#17 of 18 · shot within 12s of a regain in own half
Press→shot /game3.63
#4 of 18 · shot within 8s of a high regain
Speed to final third after regain10.6s
#3 of 18 · lower = faster break

Sequence success — attacking transition

15attacking transition sequences this season
13%succeed — produce a shot
47%turned over with nothing to show

Sequence success — defensive transition

29defensive transition sequences this season
55%succeed — reach the next third or a shot
10%turned over with nothing to show
Vision AI Model Insight
Mainz are a direct, fast-break side: 20% of possessions reach the final third in 3 passes or fewer (#2 of 18), against 21% built patiently over 6+ passes (#15 of 18). Winning the ball back leads to a shot inside 12 seconds 0.85 times a game (#17), and a high regain converts to a shot within 8 seconds 3.63 times a game (#4 of 18) — a genuine transition weapon.
Defending the box2 sections

Vulnerability Map — how opponents hurt them

The report flipped: where do opponents create their threat against Mainz? 290 opponent progressive passes and 92 shots faced (11.3 xG against) across the season.

Opponent threat by lane (attacking into Mainz)

Right wing
23%
Right half-space
13%
Central
22%
Left half-space
13%
Left wing
28%
Where the danger came from. Opponents generated most of their threat through the left wing (28% of the danger conceded).

How Do They Concede?

The question
Where do the goals against Mainz actually come from — open play, dead balls, and who supplies them?

Every goal Mainz conceded across the loaded matches — the opponent's shots in Mainz's own matches — plus every individual opponent key pass faced, each drawn as its own real arrow (not zone-clustered) with the % share of each zone behind it.

Where the goals go in

← opponent shot at own goal

Every opponent key pass against them (60 total)

2%2%2%3%5%5%2%12%25%22%10%12%Mitchell WeiserLudovit ReisChukiMitchell WeiserPatson DakaMitchell WeiserMarco GrüllAmos PieperLudovit ReisMarco GrüllMick SchmetgensCedric IttenDaniel Heuer FernandesKeita KosugiJonathan BurkardtRitsu DoanRobin KochCan UzunJeremiaha MaluzeJonathan BurkardtJonathan BurkardtMario GötzeCan UzunFlorian NeuhausFranck HonoratFlorian NeuhausFlorian NeuhausFranck HonoratJan LeszczynskiHugo BolinJoe ScallyLukas UllrichSantiago CastañedaStefano MarinoGabriel VidovicMarvin PieringerJano ter HorstLaurin CurdaRaphael ObermairRaphael ObermairAleix GarcíaMoussa DiabyAleix GarcíaIbrahim MazaAfonso MoreiraPatrik SchickMiguel GutiérrezNathan TellaAleix GarcíaMalik TillmanKevin StögerIsac LidbergKevin DiksKevin StögerWael MohyaTimo BeckerKenan KaramanRobin GosensHasan KuruçayMax Wöber← own goal

By situation

Open play
10 · 77%
Set pieces
0 · 0%
Penalties
3 · 23%
Vision AI Model Insight
13 goals conceded. 77% come in open play; set pieces account for only 0%, so dead-ball defending is not the main issue. 8% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right half-space of the attacking third (25% of key passes faced) — that is the zone to shut off.
Context

Shape and tactics

Set pieces, strengths and weaknesses, and who they resemble.

Premium
Tactical Phase Read
The tactical idea behind each phase of this team's play, written from the same numbers shown above — what they do in build-up, how they progress, where they create, and how they defend it.
Subscribe →

Set-Piece Threat

How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.

Open play
13 g · 11.3 xG
Set pieces
3 g · 2.5 xG
Penalties
0 g
19% of goals come from set pieces (3 of 16), worth 2.5 xG from 23 shots. Set pieces are a secondary threat; the danger is in open play.

Where do Mainz's corners go?

35 corners taken this season, split by which flag they were taken from — arrows from the flag, dots at the delivery point, shaded by % of that side's corners landing in each zone (same treatment as the concede map below). No swing-direction (inswinger/outswinger) data is recorded here, only a start and end point per pass, which cannot show how the ball curved — so this shows what is genuinely knowable: where it lands, and short vs actually delivered. Tap or hover a dot for who took it and the match; filter to one match below and the shading recomputes for just that game.

Right side (17)

6%12%29%24%6%
Delivered into the box 76%Short 24%

Left side (18)

6%6%6%11%17%28%11%17%
Delivered into the box 100%Short 0%

Where opponents attack Mainz from set pieces

15 shots from opponent corners against Mainz, 0 conceded, split by the flag the corner came from — arrows from the flag, dots where the shot was taken (gold = goal), shaded by the % of that flag's shots in each zone. Tap or hover a shot for who took it, the minute and the match; filter to one match and the shading recomputes for just that game.

Right flag (9)

22%22%56%
Shot 9Goals 0

Left flag (6)

17%83%
Shot 6Goals 0

Tactical Strengths & Weaknesses

Strengths (top-third vs league)
  • field tilt % — 60.34 (83th pct)
  • progression /game — 59.71 (83th pct)
  • threat created /game — 14.01 (83th pct)
  • xG created /game — 2.09 (78th pct)
  • shots /game — 17.71 (78th pct)
Weaknesses (bottom-third vs league)
  • none stand out

Style Archetype & Similar Teams

Team archetype: High-press possession side. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.

Bayern
76%
Hoffenheim
74%
Stuttgart
59%
Leverkusen
41%
Borussia Dortmund
38%

Performance vs Opponent Styles

The question
Not just how Mainz results against a high press or a low block — how does their own play actually change?

Every match split into three tiers by how hard that specific opponent pressed (opponent PPDA — lower means a more aggressive press), matching Mainz's own passing behaviour to the result.

Results

opponent typegoals for/ggoals against/gpoints/g
vs high-pressing opponents (2m)3.000.003.00
vs mid-block opponents (2m)3.503.501.50
vs low-block opponents (3m)1.002.000.33

Behaviour — how Mainz actually play against each

opponent typeretentiondirectness (long balls)prog. passes/gxT/gbox entries/g
vs high-pressing opponents74%19%4611.313.5
vs mid-block opponents81%11%5914.322.0
vs low-block opponents81%12%6915.619.7
Vision AI Model Insight
Facing a high press, retention drops to 74% vs 81% against a low block (7pt lower under pressure), and directness (long-ball share) sits at 19% when pressed vs 12% against a low block — a real style switch: they go noticeably more direct when pressed rather than trying to play through it.

Scouting Summary — the team in brief

In brief. Mainz are possession-dominant with a aggressive high-press. Their attack creates 2.09 xG a game and they concede 1.61 a game. [Event-data based — no tracking, so off-ball structure and marking scheme need video.]
Squad

Players

Who drives the numbers.

On-Ball Value — who moved the game

Every pass, carry, take-on, shot and ball win is worth the change it made to the chance of scoring minus the chance of conceding in the next 10 seconds. This is Mainz’s last 7 matches added up: where the value came from, who produced it, and the six biggest single moves. OBV only measures what a player does with the ball: it shows who added the most on-ball value, not who was the most important player (defending off the ball, positioning and pressing are not in it).

+14.1on-ball value, 7 matches
Where the value came from
Passes3557 actions+7.39
Carries660 actions+0.83
Dribbles99 actions-0.11
Shots124 actions+3.66
Ball wins440 actions+2.31
Most on-ball value (OBV)ActionsOBVper 100
Nadiem Amiri548+2.87+0.52
Anthony Caci398+2.85+0.72
Sheraldo Becker261+2.38+0.91
Stefan Posch512+1.17+0.23
Kaishu Sano477+1.15+0.24
Kacper Potulski461+0.97+0.21
Danny da Costa317+0.87+0.27
Fabio Gruber432+0.81+0.19
123456
Biggest single movesOBV
1  Nadiem Amiri · shot+0.770
2  Sheraldo Becker · pass+0.556
3  Phillip Tietz · shot+0.550
4  Anthony Caci · shot+0.502
5  Kaishu Sano · pass+0.425
6  Anthony Caci · pass+0.393

Set Pieces by Player

Who delivers Mainz’s dead balls and who finishes them this season. 35 corners taken, most by Nadiem Amiri (34); 23 shots from corners and free kicks, 3 goals, led by Anthony Caci (1 goals, 0.70 xG). “Into the box” counts corners that landed inside the penalty area; free-kick takers are the players who shot from one, because this feed does not mark who delivers the others.

PlayerCorners takenInto the boxSet-piece shotsGoalsxGFree-kick shotsPenalties
Nadiem Amiri3426 (76%)2—0.0910/1
Danny da Costa——410.49——
Kacper Potulski——4—0.49——
Sheraldo Becker——3—0.08——
Fabio Gruber——2—0.13——
Eric Martel——210.29——
Lee Jae-Sung10 (0%)1—0.07——
Benedict Hollerbach——1—0.03——
Stefan Posch——1—0.04——
Anthony Caci——110.70——
Kaishu Sano——1—0.04——
Phillip Tietz——1—0.04——

Top Performers

Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 189 minutes). Coloured chips are that player's strongest capability blocks.

playerposminVision AI Scorecapability blocks
Nadiem AmiriAM6357.8Box Threat 76%Chance Creation 65%Ball-Carrying 65%
Kaishu SanoCM6355.7Successful Dribble 74%Ball-Carrying 65%Progression 48%Chance Creation 41%
Fabio GruberCB4714.9Ball-Carrying 83%Ball-Winning 78%Progression 71%Aerial & Box Defending 34%
Anthony CaciW5104.0Chance Creation 60%Wide Delivery 44%Box Threat 37%Ball-Carrying 31%
Kacper PotulskiCB5553.9Ball-Winning 48%Progression 36%Aerial & Box Defending 28%Ball-Carrying 17%
Phillip TietzST6563.6Ball-Carrying 40%Finishing 25%Box Threat 25%Aerial Threat 0%
Stefan PoschCB5613.5Ball-Carrying 63%Ball-Winning 56%Progression 54%Aerial & Box Defending 16%
Phillipp MweneW6403.3Wide Delivery 36%Chance Creation 36%Box Threat 35%Ball-Carrying 26%
Sheraldo BeckerST5172.2Ball-Carrying 61%Finishing 33%Box Threat 14%Aerial Threat 0%
Alexander SchwolowGK4731.7Shot-Stopping 17%

Top Creators

Chance creation leaders — key passes /90 and box entries.

playerposminkey P /90box entriesxT
Nadiem AmiriM6353.264017.5
Anthony CaciM5102.472312.0
Sheraldo BeckerF5172.61163.9
Lee Jae-SungM4381.8583.4
Kaishu SanoM6351.4257.6
Phillip TietzF6560.6982.4
Danny da CostaD3750.9686.7
Phillipp MweneM6400.7056.6

Wide Delivery Leaders

Crosses and cutbacks — wingers and full-backs only (by real average position, not the raw D/M position code), ranked by combined volume.

playerposmincrosses (completed)cutbacks (completed)
Anthony CaciW51015 (40%)8 (25%)
Danny da CostaFB37512 (33%)3 (33%)
Phillipp MweneW6404 (50%)5 (20%)

Defensive Volume Leaders

Who does the defensive work — tackles, interceptions, recoveries.

playerposmindef actionstacklesinterceptrecoveries
Phillip TietzF6561402121
Kaishu SanoM63512692344
Kacper PotulskiD55512211616
Fabio GruberD47111512824
Stefan PoschD56111510723
Phillipp MweneM64010014428
Anthony CaciM51087151022
Lee Jae-SungM4387610813

Full Squad Data

Every player with 100+ minutes (15 players).

playerposappsmingoalsxGkey PprogdefxT
Phillip TietzF765653.9581402.4
Phillipp MweneM764011.45371006.6
Nadiem AmiriM763501.723855717.5
Kaishu SanoM763500.210281267.6
Stefan PoschD656100.114111511.2
Kacper PotulskiD755500.61241226.8
Sheraldo BeckerF751722.11522463.9
Anthony CaciM751031.414578712.0
Alexander SchwolowG547300.0124135.5
Fabio GruberD547100.11251157.7
Lee Jae-SungM743810.5914763.4
Danny da CostaD737510.6427756.7
Ransford KönigsdörfferM718810.612300.7
Robin ZentnerG218700.0010112.4
Eric MartelM411610.500220.2

Player Role Architecture

The question
Who does what — and what tactical job does each key player actually perform?

The nine most-used players (450+ minutes), each with the role their event fingerprint points to — not just their listed position.

Phillip Tietz
Poacher / finisher · Forward
minutes656
goals · xG5 · 3.9
prog passes /901.1
key passes /900.69
def actions /9019.2
pass accuracy57%
Phillipp Mwene
Ball-winner / destroyer · Midfielder
minutes640
goals · xG1 · 1.4
prog passes /905.2
key passes /900.70
def actions /9014.1
pass accuracy75%
Nadiem Amiri
Advanced creator · Attacking mid
minutes635
goals · xG0 · 1.7
prog passes /9012.0
key passes /903.26
def actions /908.1
pass accuracy81%
Kaishu Sano
Ball-winner / destroyer · Midfielder
minutes635
goals · xG0 · 0.2
prog passes /904.0
key passes /901.42
def actions /9017.9
pass accuracy89%
Stefan Posch
Ball-playing defender · Defender
minutes561
goals · xG0 · 0.1
prog passes /906.6
key passes /900.16
def actions /9018.4
pass accuracy86%
Kacper Potulski
Stopper / marker · Defender
minutes555
goals · xG0 · 0.6
prog passes /903.9
key passes /900.16
def actions /9019.8
pass accuracy90%
Sheraldo Becker
Advanced creator · Attacking mid
minutes517
goals · xG2 · 2.1
prog passes /903.8
key passes /902.61
def actions /908.0
pass accuracy69%
Anthony Caci
Advanced creator · Attacking mid
minutes510
goals · xG3 · 1.4
prog passes /9010.1
key passes /902.47
def actions /9015.4
pass accuracy76%
Alexander Schwolow
Goalkeeper · Goalkeeper
minutes473
goals · xG0 · 0.0
prog passes /904.6
key passes /900.19
def actions /902.5
pass accuracy67%

Recruitment Intelligence — profiles to sign

The question
If you were sporting director, which player profiles would move the needle most?

Each recruitment target is derived directly from the team's weakest areas vs the league — the gap, why it shows in the data, and the profile that closes it.

Need — below-average: xG conceded /game (44th pct)
Sign a defensive organiser / shot-suppressing centre-back. The data flags this because too much xG is conceded (1.61, 44th percentile vs the league).
Need — below-average: pass retention % (50th pct)
Sign a secure ball-retaining midfielder. The data flags this because possession is turned over cheaply (79.36, 50th percentile vs the league).
Vision AI · building report