Combining every season — this one is larger, give it a moment.
Team reportPremier League 2025/26
Nottingham Forest
A balanced-territory side, aggressive high-press out of possession, with a moderate attack. Finished #16 of 20 on 44 points (11-11-16, 48:51). Tactical profile below is built from 38 matches of ball-by-ball event data, contextualised against the league.
In one read, what kind of team is Nottingham Forest and how good was the season?
Read. Nottingham Forest finished #16 of 20 on 44 points (11W-11D-16L, 1.16 pts/game), scoring 48 and conceding 51 for a goal difference of -3.
On the ball they are a balanced-territory team (48% field tilt, #12 in the league); off it they defend with a aggressive high-press (#17 of 20 for press intensity).
Across the 38 matches of event data, they create 1.17 xG/game (#16) and concede 1.41 (#12), a net of -0.25 xG/game — the underlying performance matches the actual goal difference.
Season Context — the league table
The question
Where did Nottingham Forest actually finish, and against whom?
The authoritative final table (full season), centred on Nottingham Forest.
#
team
P
W
D
L
GF
GA
GD
Pts
13
Everton
38
13
10
15
47
50
-3
49
14
Leeds
38
11
14
13
49
56
-7
47
15
Crystal Palace
38
11
12
15
41
51
-10
45
16
Nottingham Forest
38
11
11
16
48
51
-3
44
17
Tottenham
38
10
11
17
48
57
-9
41
18
West Ham
38
10
9
19
46
65
-19
39
19
Burnley
38
4
10
24
38
75
-37
22
Match Momentum — when they create and concede danger
The question
Across a whole game, which periods does Nottingham Forest 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)
Vision AI Model Insight
Nottingham Forest tend to fade after a strong start — first-half net xG -2.47, second-half -6.88. Their strongest period is 31-45' (net -0.23 xG); their most dangerous period to face is 46-60' (net -3.25 xG against them).
xG Race — the season, match by match
Expected goals piling up over 38 matches: Nottingham Forest 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 (-9.3 xG). Hover a point for that match.
Nottingham ForestOpponents against Nottingham Forest
Tactical DNA
The football question
In five genes, what is Nottingham Forest'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.
42
Build-Up
possession control from the back
45
Progression
moving the ball upfield with threat
28
Creation
manufacturing chances
28
Press
winning the ball high & hard
40
Solidity
resisting the opponent's threat
Genetic identity
Nottingham Forest are defined most by their Progression DNA (45/100 · average at moving the ball upfield with threat), and least by their Press DNA (28/100). Percentiles vs the 20 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).
Possess
Deep buildup
22%
Possession
43%
Field tilt
43%
Disrupt
Central progression ~proxy
52%
Dribble tendency
70%
Progressive passes
61%
Finish
Final-third patience
26%
Shot quality
13%
Open-play xG
48%
Press
Forward press ~proxy
22%
High regains
52%
Pressing efficiency ~proxy
13%
Defend
Def-mid press ~proxy
39%
High line ~proxy
22%
PAdj defensive actions
39%
Nottingham Forest is defined most by Disrupt (61% in the league) and weakest on Finish (29%). Percentiles vs the 23 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.
How much of the pitch does Nottingham Forest actually control, relative to the rest of the league?
Field tilt = share of final-third touches between the two sides across Nottingham Forest'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; Nottingham Forest highlighted. Hover any dot for the team.
Vision AI Model Insight
Nottingham Forest post 48% field tilt, ranking #12 of 20 in the league — below the league mid-point, spending more of the game defending their own territory than dictating it, and their 1.16 points/game sits below the league-average return — territory is genuinely converting into results.
Where the time goes — how long each phase lasts
The question
When Nottingham Forest has the ball, how much of that time is spent building, progressing and creating?
The share of Nottingham Forest'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.
Phase
Share of the time
Build-up
25.8%
Progression
42.5%
Chance creation
28.9%
Attacking transition
2.8%
Vision AI Model Insight
Progression is where Nottingham Forest spends most of its time on the ball (42% 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.
width = passes between the paircolour = xT per pass: low → high
Top 10 combinations
Williams ↔ Anderson
570p · 21.8xT
Gibbs-White ↔ Anderson
429p · 27.5xT
Milenkovic ↔ Anderson
382p · 13.5xT
Hudson-Odoi ↔ Anderson
310p · 20.2xT
Williams ↔ Murillo
294p · 12.8xT
Milenkovic ↔ Murillo
286p · 4.5xT
Anderson ↔ Murillo
277p · 11.3xT
Sangaré ↔ Anderson
267p · 8.6xT
Sels ↔ Milenkovic
238p · 8.0xT
Hudson-Odoi ↔ Williams
235p · 12.7xT
Vision AI Model Insight
Busiest connection: Neco Williams ↔ Elliot Anderson (570 passes). Most dangerous per pass: Morgan Gibbs-White ↔ Murillo (0.10 xT/pass over 100 passes) — the combination that hurts opponents most when it fires, not just the one used most.
Touch Density — where Nottingham Forest had the ball
Every touch across 380 matches. Brighter means more touches.
Which Zones Do They Attack Through?
The question
Across the whole pitch, where does Nottingham Forest 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
17145
ACTIONS
Carries
the ball moved with a player
3991
ACTIONS
Vision AI Model Insight
Their single busiest zone is the left wing of the middle third (10.1% of all actions). 56% of actions run through the inside lanes (centre + both half-spaces) versus 44% down the two wings — an inside-dominant, half-space build. By third: 29% defensive, 46% middle, 25% 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. Nottingham Forest leaned on their left (42%). The bars under the pitch put the league’s share beside theirs.
Nottingham Forest
42%20%38%
League
39%23%38%
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. Nottingham Forest made 1522 this season (40.1 a match); 31% came into the left wing. Longer, bolder arrows mean more entries. Entries by carrying the ball in count as well as completed passes; dribbles are not counted.
Channel
Entries
Per match
Share
League share
Left wing
478
12.6
31%
29%
Left half-space
229
6.0
15%
16%
Centre
160
4.2
11%
12%
Right half-space
185
4.9
12%
14%
Right wing
470
12.4
31%
29%
Build-up2 sections
Build-Up — the pass map
The football question
How does Nottingham Forest 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.
5155build-up passes attempted
77%completed
28%reach the middle third
2%go long into the final third
Pass types
1930CB · 49%
249Long Wide · 6%
66Long Central · 2%
1384Wide → FB · 35%
348Central · 9%
Sequence success — build-up
1289build-up sequences this season
9%succeed — reach the next third or a shot
44%turned over with nothing to show
CB1930 · 49%
Long Wide249 · 6%
Long Central66 · 2%
Wide → FB1384 · 35%
Central348 · 9%
◆ Vision AI Insight
Nottingham Forest build predominantly down the left, with Milenkovic as the fulcrum (648 build-up passes). Only 30% 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 → LHS def (237×).
Pass Explorer
Every pass of Nottingham Forest’s last 10 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.
From channel
To channel
From third
To third
Outcome
Minutes
Channel to channel · click a cell
Third to third · click a cell
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. 1568 progressive passes across 38 matches (41.3/game).
Origin lane
Right wing
32%
Right half-space
12%
Central
15%
Left half-space
12%
Left wing
29%
Progression is right-dominant: 44% right side · 15% central · 41% left.
Who generates it
Elliot Anderson
261 · 17%
Neco Williams
193 · 12%
Matz Sels
124 · 8%
Ola Aina
110 · 7%
Murillo
103 · 7%
Top two — Elliot Anderson & Neco Williams — account for 29% of all progression.
PROGRESSION
41.3 prog passes/game
mostly right
→
PENETRATION
27% reach final third
21% reach the box
→
THREAT
the other 73% is recycled
progress ≠ danger
What came of it.27% of progressive passes reached the final third and 21% the box; the rest was recycled in front of the block. 29% 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
Centralbalanced
Left half-spacebalanced
Left wingbalanced
Progression — the pass map
The football question
Once Nottingham Forest 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.
7729progression passes attempted
84%completed
17%break into the final third
11%go backward into the defensive third
Pass types
2109CB · 32%
221Long Wide · 3%
36Long Central · 1%
3319Wide → FB · 51%
823Central · 13%
Sequence success — progression
1782progression sequences this season
27%succeed — reach the next third or a shot
42%turned over with nothing to show
CB2109 · 32%
Long Wide221 · 3%
Long Central36 · 1%
Wide → FB3319 · 51%
Central823 · 13%
◆ Vision AI Insight
Nottingham Forest progress predominantly down the left, with Anderson the busiest midfield outlet (1236 progression passes). Only 17% of passes that start in midfield actually break the final-third line — the rest (72%) is recycled back into midfield or (11%) played backward, the hallmark of a side that carries progression itself through midfield rather than just passing through it. Their most-repeated pattern is LHS mid → LW mid (355×) — 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)
14%
Progressive
1%
Neutral / sideways
49%
Safe / backward
36%
15% of passes are progressive-or-better; the remaining 85% keep possession without advancing it. A control-first side — safe in build-up, reliant on a few players for the incision.
Where does Nottingham Forest actually progress the ball with their feet, not a pass?
913 progressive carries this season across the whole squad (23% of 3896 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 55m. Most active carrier: Anderson (109). Filter to one player below.
First (256)Second (254)Third (204)Fourth (199)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 Nottingham Forest'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.
476shots
44.4xG
0.09xG per shot
17big chances (0.3+ xG)
47goals
#10key passes / game in the league
#15box entries / game in the league
Kind of attack, then how the shot was set up
Shots
Goals
xG
xG / shot
Attacking transition25% of shots
117
7
11.88
0.10
Cross
7
0
0.85
0.12
Cut-back
7
2
0.65
0.09
Long ball
4
1
0.61
0.15
Pass into the box / short pass
62
3
6.70
0.11
Own creation (carry, dribble, rebound)
37
1
3.06
0.08
Settled attack55% of shots
264
29
23.97
0.09
Cross
56
4
5.57
0.10
Cut-back
30
3
3.38
0.11
Long ball
12
1
0.93
0.08
Pass into the box / short pass
106
13
8.80
0.08
Own creation (carry, dribble, rebound)
60
8
5.30
0.09
Set piece20% of shots
95
11
8.55
0.09
Corner
79
7
5.82
0.07
Free kick
13
1
0.35
0.03
Penalty
3
3
2.38
0.79
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
Player
Set-ups
xG created
Goals
Morgan Gibbs-White
45
3.92
3
Callum Hudson-Odoi
42
3.37
4
Neco Williams
23
2.69
2
Omari Hutchinson
26
2.60
4
Elliot Anderson
33
2.37
2
Who takes them
Player
Shots
xG
Goals
Morgan Gibbs-White
83
9.29
15
Igor Jesus
70
7.44
6
Elliot Anderson
44
3.33
4
Chris Wood
23
3.21
3
Callum Hudson-Odoi
30
3.08
3
Vision AI Model Insight
The biggest source of Nottingham Forest's chances, by xG, is settled attack (24.0 of 44.4 xG, 264 of 476 shots), mostly pass into the box / short pass. 117 of the 476 shots (25%) came within 15 seconds of winning the ball. 95 of the 476 shots came from set pieces; 47 goals from 44.4 xG (+2.6).
Chance Creation — the pass map
The football question
How does Nottingham Forest 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
49CB · 15%
19Long Wide · 6%
27Long Central · 8%
88Wide → FB · 27%
148Central · 45%
Sequence success — final-third
1177final-third sequences this season
23%succeed — produce a shot
41%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
CB49 · 15%
Long Wide19 · 6%
Long Central27 · 8%
Wide → FB88 · 27%
Central148 · 45%
Who creates — volume × threat
53Anderson
48Gibbs-White
43Hudson-Odoi
34Williams
31Hutchinson
14Jesus
13Ndoye
331key passes #10 of 20
11.9box entries/game #15 of 20
31.4final-3rd entries/game #10 of 20
1.17xG/game #16 of 20
47goals #12 of 20
+2.6finishing vs xG #8 of 20
◆ Vision AI Insight
Nottingham Forest lean left — 43% of their key passes come from that flank and its half-space (141 of 331), most often from Hudson-Odoi (37). They funnel chances into the c of the final third, most often via RW att → C att (53 key passes, most often from Anderson). Anderson is the primary source overall. They generate 1.17 xG a game and finish it clinical — they beat their xG, led by Morgan Gibbs-White (15G), Igor Jesus (6G), Elliot Anderson (4G).
Crosses & Cutbacks — squad-wide
The question
How does Nottingham Forest deliver into the box from wide areas, and what comes of it?
595 crosses: 167 found a team-mate (28%), 64 led to a shot (11%). 119 cut-backs: 63 found a team-mate (53%), 37 led to a shot (31%). 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 Nottingham Forest actually cross from?
595 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. 23% come from one territory, the byline right wing; 34 come from the half-spaces and 0 from the centre.
Territory
Crosses
Share
Led to a shot
byline · right wing
139
23%
20
byline · left wing
138
23%
14
advanced · right wing
123
21%
17
advanced · left wing
102
17%
6
deep · left wing
35
6%
1
deep · right wing
24
4%
0
advanced · right half-space
8
1%
0
advanced · left half-space
8
1%
1
byline · left half-space
8
1%
4
byline · right half-space
5
1%
1
deep · right half-space
4
1%
0
deep · left half-space
1
0%
0
Finishing3 sections
Shot Map
476 shots for 44.4 xG (1.17/game, #16 in the league). Click an area of the box to see who shoots from there; filter by outcome, player or match.
Moderate attack. 12.5 shots/game at 0.093 xG/shot. Box entries 12/game (#15). Threat created 10.0 xT/game (#11).
Goal Sequences
The move behind each of Nottingham Forest’s 48 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 — 48 goals traced.
How Do They Score?
The question
Where do the goals actually come from — open play, dead balls, which foot?
Every goal Nottingham Forest scored across the loaded matches, broken down by how it was created and finished.
By situation
Open play
36 · 77%
Set pieces
8 · 17%
Penalties
3 · 6%
By finish
Right foot
28 · 60%
Left foot
9 · 19%
Header
10 · 21%
Vision AI Model Insight
47 goals scored. 77% come in open play; set pieces add 17%. 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
How they lose it
Incomplete pass — attacking third
1198
Failed forward pass
776
Failed take-on
425
Lost in the defensive third
1178
Their weakest zone is the right wing in the attacking third: 31% of 1241 pass attempts there are lost (386 times). Separately, 33% 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.
How hard does Nottingham Forest 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.
26
Pressing Index /100 · passive
Intensity18
PPDA, high-press share, high-zone actions
Height25
how high up the pitch they defend
Effectiveness25
high recoveries & high-regain share
Disruption40
opponent completion & progression allowed
Every defensive action & where it happens
Where they win the ball back — by lane
RW
19%
RHS
20%
C
21%
LHS
19%
LW
21%
By third (high / mid / low regain)
Def 54%
Mid 33%
Att 13%
Read
Ranked #17 of 20 for press intensity, this is a high press. Note the tension: 54% of their raw defensive actions still occur in their own third — a possession effect, not a passive one. Dominating the ball (19% high-press share) means that when they do defend, it is often deep. Of recoveries, 50% are won high up the pitch.
Every pressing metric, ranked in the league
metric
value
rank
meaning
PPDA (opp passes per def action)
—
#17/20
lower = more intense; rank is what matters
Defensive actions / game
139
#12/20
High-press share
19%
#15/20
def actions won high up
Defensive-action height (0–100)
35.7
#15/20
avg pitch height of actions
High recoveries / game
21.3
#13/20
ball won in mid+att third
High-recovery share
50%
#16/20
share of recoveries won high
Opponent completion allowed
82%
#16/20
lower = more disruptive
Opponent progressive allowed
8.5%
#8/20
of 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 Nottingham Forest win it back with a defensive action?
Counted over the 1204 opposition possessions that Nottingham Forest 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 Nottingham Forest step in. Nottingham Forest win it after 6.1 opponent passes in the first half-hour and after 4.5 after the hour mark (sooner).
PASSES THEY GET
5.2passes
median 3 · before Nottingham Forest win it
TIME THEY KEEP IT
16sec
median 8s · 31.7 wins per game
How many passes the opponent got in before the ball was won
41%
28%
15%
16%
0–2 passes3–5 passes6–9 passes10+ passes
Through the match — every 15 minutes
0–15′
5.7
17s
15–30′
6.6
21s
30–45+′
5.1
15s
45–60′
5.0
15s
60–75′
4.9
14s
75–90+′
4.1
13s
average passesaverage seconds
Phases of play
Every moment of the match read twice — what Nottingham Forest were doing with the
ball, and what the opposition were doing about it at that exact moment. Built from the
real action sequence in time order, so one possession is split across the phases it
genuinely passed through rather than labelled once by where it began.
6,774phase segments analysed
7.0smedian time before the opposition engage
2.0their passes per defensive action when pressed
#15of 20 for share of defending spent pressing high
How Nottingham Forest defend when the opponent has it.
High press 6.9%Mid block 11.9%Low block 50.6%Unopposed 30.6%
What opponents do against Nottingham Forest
The mirror image, and the part no other section can show: whether teams come
out to press Nottingham Forest or sit in and invite them on. A side that is always faced with a low
block is playing a different sport from one that is pressed every week.
High press 8.6%Mid block 10.9%Low block 52.8%Unopposed 27.6%
Which phases actually produce
Phase
Segments
Avg length
Avg actions
Ends in shot
Turned over
Build-up
2,240
4.4s
2.8
0.4%
54.3%
Progression
2,727
6.0s
3.7
0.5%
48.7%
Chance creation
1,668
6.6s
3.7
25.6%
53.1%
Attacking transition
139
7.7s
4.7
1.4%
42.4%
Most common routes through the phases
How a possession actually travelled, in order. The chain is the tactical signature — a move that climbs all three thirds is a different thing from one that never leaves the first.
Route
Times
Ends in shot
build_up
1,022
0.3%
progression
729
0.1%
chance_creation
398
34.4%
build_up → progression
382
0.3%
progression → chance_creation
341
26.4%
build_up → progression → chance_creation
195
29.7%
Attitude in each third
Every other style number here says how much Nottingham Forest 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 Nottingham Forest 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) 5,155 passes
nothing unusual in this zone
Width of use+0.6922.42
Wide-channel share+0.650.35
Side bias+0.120.60
Pass length+0.1323.16
Long-ball share+0.330.26
Forward gain per pass+0.3212.85
Backward-pass share-0.060.20
Deep-pass share+0.260.31
Completion-0.370.76
Seconds between passes-0.153.24
Side used
left 31% · central 41% · right 28%
Progression (middle third) 7,729 passes
nothing unusual in this zone
Width of use+0.4026.43
Wide-channel share+0.400.43
Side bias+0.331.37
Pass length-0.0417.18
Long-ball share+0.030.10
Forward gain per pass+0.374.17
Backward-pass share-0.200.32
Deep-pass share+0.300.18
Completion-0.210.83
Seconds between passes-0.213.11
Side used
left 38% · central 27% · right 34%
Final third 4,261 passes
nothing unusual in this zone
Width of use+0.3332.43
Wide-channel share+0.390.62
Side bias+0.160.67
Pass length+0.2215.80
Long-ball share+0.010.10
Forward gain per pass-0.170.82
Backward-pass share+0.380.41
Deep-pass share+0.120.09
Completion+0.140.71
Seconds between passes+0.033.42
Side used
left 44% · central 14% · right 42%
Reference measured across roughly 3,500 match-teams
in this competition, per third, so the comparison is with Nottingham Forest'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 Nottingham Forest 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 share16%
#13 of 20 · reaches final third in ≤3 passes
Patient build-up share25%
#11 of 20 · spells of 6+ passes
Tempo (possession length)16.8s
#8 of 20 · shorter = faster tempo
Passes per possession4.2
#11 of 20
Counter-attack shots /game1.26
#2 of 20 · shot within 12s of a regain in own half
Press→shot /game2.83
#13 of 20 · shot within 8s of a high regain
Speed to final third after regain12.3s
#9 of 20 · lower = faster break
Sequence success — attacking transition
132attacking transition sequences this season
20%succeed — produce a shot
39%turned over with nothing to show
Sequence success — defensive transition
359defensive transition sequences this season
64%succeed — reach the next third or a shot
9%turned over with nothing to show
Vision AI Model Insight
Nottingham Forest are balanced between tempo and patience: 16% of possessions reach the final third in 3 passes or fewer (#13 of 20), against 25% built patiently over 6+ passes (#11 of 20). Winning the ball back leads to a shot inside 12 seconds 1.26 times a game (#2), and a high regain converts to a shot within 8 seconds 2.83 times a game (#13 of 20) — not yet a major source of chances.
Defending the box2 sections
Vulnerability Map — how opponents hurt them
The report flipped: where do opponents create their threat against Nottingham Forest? 1648 opponent progressive passes and 506 shots faced (53.8 xG against) across the season.
Opponent threat by lane (attacking into Nottingham Forest)
Right wing
30%
Right half-space
16%
Central
14%
Left half-space
14%
Left wing
24%
Where the danger came from. Opponents generated most of their threat through the right wing (30% of the danger conceded).
How Do They Concede?
The question
Where do the goals against Nottingham Forest actually come from — open play, dead balls, and who supplies them?
Every goal Nottingham Forest conceded across the loaded matches — the opponent's shots in Nottingham Forest'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
Every opponent key pass against them (363 total)
By situation
Open play
34 · 71%
Set pieces
9 · 19%
Penalties
5 · 10%
Vision AI Model Insight
48 goals conceded. 71% come in open play; set pieces account for only 19%, so dead-ball defending is not the main issue. 12% are headers — aerial defending is not where the damage comes from. Their supply line runs mainly through the right wing of the attacking third (19% of key passes faced) — that is the zone to shut off.
Context
Shape and tactics
Set pieces, strengths and weaknesses, and who they resemble.
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.
How much of the goal threat is manufactured from dead balls — a route to goal that bypasses their open-play mechanism entirely.
Open play
36 g · 35.9 xG
Set pieces
8 g · 6.2 xG
Penalties
3 g
17% of goals come from set pieces (8 of 47), worth 6.2 xG from 92 shots. Set pieces are a secondary threat; the danger is in open play.
Where do Nottingham Forest's corners go?
198 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 (103)
Delivered into the box 97%Short 3%
Left side (95)
Delivered into the box 96%Short 4%
Where opponents attack Nottingham Forest from set pieces
92 shots from opponent corners against Nottingham Forest, 8 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 (51)
Shot 51Goals 5
Left flag (41)
Shot 41Goals 3
Tactical Strengths & Weaknesses
Strengths (top-third vs league)
none stand out
Weaknesses (bottom-third vs league)
press intensity (PPDA) — 7.11 (15th pct)
xG created /game — 1.17 (20th pct)
box entries /game — 11.87 (25th pct)
Style Archetype & Similar Teams
Team archetype: Direct / counter high-press. Their closest stylistic matches in the league (cosine similarity on style metrics) — sides that play a similar way.
Wolves
84%
West Ham
83%
Burnley
75%
Sunderland
52%
Fulham
39%
Performance vs Opponent Styles
The question
Not just how Nottingham Forest 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 Nottingham Forest's own passing behaviour to the result.
Results
opponent type
goals for/g
goals against/g
points/g
vs high-pressing opponents (12m)
1.92
0.92
1.75
vs mid-block opponents (12m)
1.17
1.92
0.92
vs low-block opponents (14m)
0.79
1.21
0.86
Behaviour — how Nottingham Forest actually play against each
opponent type
retention
directness (long balls)
prog. passes/g
xT/g
box entries/g
vs high-pressing opponents
73%
20%
35
8.2
9.5
vs mid-block opponents
79%
15%
39
9.3
9.4
vs low-block opponents
82%
12%
49
12.2
16.0
Vision AI Model Insight
Facing a high press, retention drops to 73% vs 82% against a low block (9pt lower under pressure), and directness (long-ball share) sits at 20% 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. Nottingham Forest are balanced-territory with a aggressive high-press.
Their attack creates 1.17 xG a game and they concede 1.41 a game.
[Event-data based — no tracking, so off-ball structure and marking scheme need video.]
For each of ten match numbers, the one match this season that departed furthest from Nottingham Forest’s own average, the eight biggest swings first. Bar right of centre means more than usual, left means less. It shows how much the way they play moves from game to game, and which opponent moved it.
Threat from passingvs Everton · 0-2 · 2025-12-309.97avg 5.20
Pass accuracyat Liverpool · 3-0 · 2025-11-2268%avg 78%
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 Nottingham Forest’s last 12 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).
+17.0on-ball value, 12 matches
Where the value came from
Passes5056 actions+8.16
Carries1123 actions-0.51
Dribbles215 actions-0.01
Shots143 actions+5.13
Ball wins735 actions+4.22
Most on-ball value (OBV)
Actions
OBV
per 100
Elliot Anderson
1007
+2.65
+0.26
Morgan Gibbs-White
520
+2.16
+0.42
Igor Jesus
327
+1.88
+0.57
Callum Hudson-Odoi
241
+1.47
+0.61
Nicolás Domínguez
299
+1.40
+0.47
Matz Sels
389
+1.36
+0.35
Taiwo Awoniyi
76
+0.80
+1.05
Omari Hutchinson
400
+0.71
+0.18
Biggest single moves
OBV
1 Igor Jesus · shot
+0.806
2 Dilane Bakwa · pass
+0.590
3 Morgan Gibbs-White · pass
+0.527
4 Callum Hudson-Odoi · pass
+0.426
5 Matz Sels · pass
+0.359
6 Chris Wood · shot
+0.328
Set Pieces by Player
Who delivers Nottingham Forest’s dead balls and who finishes them this season. 198 corners taken, most by Elliot Anderson (87); 92 shots from corners and free kicks, 8 goals, led by Morgan Gibbs-White (2 goals, 0.65 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.
Player
Corners taken
Into the box
Set-piece shots
Goals
xG
Free-kick shots
Penalties
Elliot Anderson
87
74 (85%)
5
—
0.12
2
1/1
Neco Williams
23
20 (87%)
14
1
0.47
2
—
Omari Hutchinson
45
36 (80%)
2
—
0.06
—
—
Igor Jesus
—
—
15
1
1.25
—
1/1
Morgan Gibbs-White
1
0 (0%)
14
2
0.65
5
1/1
Nikola Milenkovic
—
—
9
—
0.55
—
—
Chris Wood
—
—
6
1
0.85
—
—
Murillo
—
—
6
1
0.38
2
—
Dan Ndoye
5
4 (80%)
3
—
0.20
—
—
Nicolás Domínguez
4
3 (75%)
3
1
0.21
—
—
Dilane Bakwa
12
10 (83%)
—
—
0.00
—
—
Douglas Luiz
9
8 (89%)
1
—
0.02
—
—
Callum Hudson-Odoi
7
5 (71%)
1
—
0.02
1
—
Ibrahim Sangaré
—
—
3
—
0.15
—
—
Top Performers
Ranked by Vision AI Score — the same tactical behavioural evaluation each player's own report page shows (min 300 minutes). Coloured chips are that player's strongest capability blocks.
Chance creation leaders — key passes /90 and box entries.
player
pos
min
key P /90
box entries
xT
Elliot Anderson
M
3526
1.35
86
66.0
Morgan Gibbs-White
M
3224
1.34
52
27.2
Callum Hudson-Odoi
M
1919
2.02
43
17.2
Omari Hutchinson
M
1811
1.54
51
15.0
Neco Williams
D
3446
0.89
45
39.0
Dan Ndoye
M
1244
0.94
19
6.7
Igor Jesus
F
2383
0.53
16
5.2
Ola Aina
D
1680
0.43
18
21.3
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.
player
pos
min
crosses (completed)
cutbacks (completed)
Callum Hudson-Odoi
W
1919
28 (21%)
37 (59%)
Ola Aina
FB
1680
19 (37%)
12 (50%)
Defensive Volume Leaders
Who does the defensive work — tackles, interceptions, recoveries.
player
pos
min
def actions
tackles
intercept
recoveries
Elliot Anderson
M
3526
703
104
41
306
Neco Williams
D
3446
560
96
47
164
Nikola Milenkovic
D
3600
538
31
29
108
Murillo
D
2252
377
35
34
102
Ibrahim Sangaré
M
2180
346
59
25
114
Igor Jesus
F
2383
340
24
3
61
Morgan Gibbs-White
M
3224
339
20
13
110
Ola Aina
D
1680
278
31
25
81
Full Squad Data
Every player with 100+ minutes (28 players).
player
pos
apps
min
goals
xG
key P
prog
def
xT
Nikola Milenkovic
D
38
3600
0
1.0
2
77
538
28.0
Elliot Anderson
M
38
3526
4
3.3
53
261
703
66.0
Neco Williams
D
37
3446
2
2.2
34
193
560
39.0
Morgan Gibbs-White
M
37
3224
15
9.3
48
93
339
27.2
Matz Sels
G
31
2848
0
0.0
1
124
58
25.7
Igor Jesus
F
37
2383
6
7.4
14
16
340
5.2
Murillo
D
25
2252
1
0.7
8
103
377
28.6
Ibrahim Sangaré
M
28
2180
2
1.0
11
66
346
19.4
Callum Hudson-Odoi
M
30
1919
3
3.1
43
92
134
17.2
Omari Hutchinson
M
31
1811
1
2.1
31
73
203
15.0
Ola Aina
D
18
1680
0
0.6
8
110
278
21.3
Morato
D
21
1464
1
1.1
2
45
239
16.2
Dan Ndoye
M
24
1244
1
1.0
13
36
149
6.7
Nicolás Domínguez
M
27
1242
1
1.4
10
49
209
13.3
Nicolò Savona
D
14
1090
2
1.1
7
50
117
10.8
Chris Wood
F
15
925
3
3.2
5
3
95
1.4
Ryan Yates
M
22
739
0
0.6
5
11
105
3.9
Dilane Bakwa
M
14
596
0
0.6
11
29
48
4.8
Taiwo Awoniyi
F
17
587
4
2.9
4
2
81
1.0
Jair Cunha
D
9
561
0
0.3
1
20
74
4.1
John Victor
G
5
458
0
0.0
0
26
8
4.9
Luca Netz
D
7
399
0
0.0
2
9
54
1.7
James McAtee
M
14
386
0
0.2
6
9
48
2.8
Oleksandr Zinchenko
D
5
375
0
0.1
5
33
63
7.5
Stefan Ortega
G
3
291
0
0.0
0
12
5
2.7
Zach Abbott
D
3
172
0
0.0
0
6
31
1.6
Lorenzo Lucca
F
4
154
1
0.6
1
0
16
0.0
Arnaud Kalimuendo
F
9
146
0
0.4
1
0
14
0.3
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.
Nikola Milenkovic
Stopper / marker · Defender
minutes3600
goals · xG0 · 1.0
prog passes /901.9
key passes /900.05
def actions /9013.5
pass accuracy87%
Elliot Anderson
Deep-lying playmaker · Midfielder
minutes3526
goals · xG4 · 3.3
prog passes /906.7
key passes /901.35
def actions /9017.9
pass accuracy82%
Neco Williams
Ball-playing defender · Defender
minutes3446
goals · xG2 · 2.2
prog passes /905.0
key passes /900.89
def actions /9014.6
pass accuracy76%
Morgan Gibbs-White
Poacher / finisher · Forward
minutes3224
goals · xG15 · 9.3
prog passes /902.6
key passes /901.34
def actions /909.5
pass accuracy79%
Matz Sels
Goalkeeper · Goalkeeper
minutes2848
goals · xG0 · 0.0
prog passes /903.9
key passes /900.03
def actions /901.8
pass accuracy62%
Igor Jesus
Ball-winner / destroyer · Midfielder
minutes2383
goals · xG6 · 7.4
prog passes /900.6
key passes /900.53
def actions /9012.8
pass accuracy72%
Murillo
Stopper / marker · Defender
minutes2252
goals · xG1 · 0.7
prog passes /904.1
key passes /900.32
def actions /9015.1
pass accuracy83%
Ibrahim Sangaré
Ball-winner / destroyer · Midfielder
minutes2180
goals · xG2 · 1.0
prog passes /902.7
key passes /900.45
def actions /9014.3
pass accuracy86%
Callum Hudson-Odoi
Advanced creator · Attacking mid
minutes1919
goals · xG3 · 3.1
prog passes /904.3
key passes /902.02
def actions /906.3
pass accuracy80%
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 — bottom-third: press intensity (PPDA) (15th pct)
Sign a high-energy presser to lift defensive intensity. The data flags this because the press is passive (7.11, 15th percentile vs the league).
Need — bottom-third: xG created /game (20th pct)
Sign a higher-volume goalscorer or chance creator. The data flags this because the attack under-produces xG (1.17, 20th percentile vs the league).
Need — bottom-third: box entries /game (25th pct)
Sign a penetrative wide player or runner to create box entries. The data flags this because few entries into the box (11.87, 25th percentile vs the league).
← BACK TO SEARCHSeason
