Aston Villa 3:1 Nottingham Forest: xG & match report · Premier League

Saturday, 03/01/2026 at 13:30

Full time

Match report · Premier League

Aston Villa Home 1.59 xG · 11 shots

Nottingham Forest Away 0.89 xG · 10 shots

31 The scoreline reflected the chances

Goals

45' Ollie Watkins Aston Villa · 0.04 xG chance 1–0
49' John McGinn Aston Villa · 0.08 xG chance 2–0
61' Morgan Gibbs-White Nottingham Forest · 0.38 xG chance 2–1
73' John McGinn Aston Villa · 0.48 xG chance 3–1

The shots behind it

Aston Villa Nottingham Forest

Shots: 11; 10
On target: 4; 5
Inside the box: 5; 6
xG per shot: 0.15; 0.09
Open play xG: 1.55; 0.83
Set-piece xG: 0.14; 0.02
Penalties: —; —

Shot-level xG may differ from the published match total

The provider publishes the match total separately from its shot-by-shot values. Those values are rounded to two decimals and, leaving out penalties and own goals, add up to its non-penalty xG: 1.69 for Aston Villa and 0.89 for Nottingham Forest.

Shot map

Both teams shown attacking the same goal for comparison.

All shots

21 shots · 2.54 xG

Hover, focus or tap a shot to read it.

Aston Villa
Nottingham Forest
Filled = goal
Dot area = xG (the smallest chances drawn at a minimum size)

Recent form

Last 6 · xG per match

Aston Villa

1.77 xG created 1.95 xGA

W 2.41 created, 2.21 conceded against Brighton & Hove Albion
W 2.16 created, 1.92 conceded against Arsenal
W 0.67 created, 1.03 conceded against West Ham United
W 1.54 created, 1.33 conceded against Manchester United
W 1.19 created, 2.14 conceded against Chelsea
L 2.67 created, 3.04 conceded against Arsenal

Nottingham Forest

0.99 xG created 1.09 xGA

W 0.74 created, 0.91 conceded against Wolverhampton
L 0.53 created, 1.12 conceded against Everton
W 1.91 created, 0.37 conceded against Tottenham Hotspur
L 0.60 created, 1.50 conceded against Fulham
L 0.59 created, 1.39 conceded against Manchester City
L 1.55 created, 1.26 conceded against Everton

xG created xG conceded (xGA) W / D / L: the match result

Model details

Show

The model never priced this match before kick-off, so there is no projection to read the result against.

Limitations

The model reads expected goals and nothing else. It does not know the lineups, who is injured or suspended, what either side has to play for, the weather, or how congested the schedule around this match is.