Premier League · England · Regular Season - 2

Liverpool–Nottingham Forest

2–2

HT 0–1

Kick-off: Saturday, 29 August 2026 at 11:30
Status: Match Finished
Venue: Anfield, Liverpool

What the model said, and what happened

Ended level — we gave the draw 21%double chance: correct

This verdict goes into the public record unchanged; we do not filter out the ones we get wrong. The model's track record

Goals and red cards

24'Nottingham ForestD. Ndoye (assist: M. Gibbs-White)
60'LiverpoolA. Isak (assist: C. Gakpo)
70'Nottingham ForestM. Gibbs-White (penalty)
82'LiverpoolVíctor Muñoz (assist: F. Wirtz)

Match statistics

Match statistics
Liverpool		Nottingham Forest
13	Shots	12
4	Shots on target	3
8	Shots inside the box	11
1	Goalkeeper saves	2
3	Corners	5
3	Offsides	0
69%	Possession	31%
631	Passes	288
562	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Liverpool 1.89 · Nottingham Forest 0.99. On the pitch: 2-2.
Shots Liverpool 13 · Nottingham Forest 12; on target 4 and 3.
Liverpool kept the ball more (69%).

Unavailable players

Liverpool

C. Bradleyout · Knee Injury
F. Chiesaout · Back Injury
G. Leoniout · Knee Injury
H. Ekitikeout · Achilles Tendon Injury
H. Elliottout · Coach's decision
J. Gomezout · Muscle Injury
S. Bajceticout · Hamstring Injury

Nottingham Forest

I. Sangareout · Calf Injury
N. Savonaout · Knee Injury
M. Gibbs-Whitedoubtful · Knee Injury
R. Yatesdoubtful · injury

Provider data — may change until the line-ups are announced. Last updated: Thursday, 17 September 2026 at 06:11.

Referee

Samuel Barrott, England→

5.50

yellows per match

0.00

reds per match

matches of data

2 matches is a small sample — these averages are noisy and should not be read as a tendency.

Model prediction

Why this prediction?

The model puts the most likely outcome at 63.5%: “Liverpool to win”. Second comes “a draw” at 21.4%.
Behind that distribution are the expected goals: 2.10 for Liverpool, 0.97 for Nottingham Forest.
The highest double chance value is 84.9% (1X — Liverpool win or draw).
These values were computed from 1,531 past matches.

This is a prediction, not a guarantee; a probability is not a certainty.

These sentences are built by a fixed template; every number in them comes straight from the model's computation, none invented while the text was written.

Liverpool to win63.5%

Draw21.4%

Nottingham Forest to win15.1%

When the model has said 60–65% in the past, 847 of 1,354 predictions were correct (62.6%). Accuracy record →

13	Fouls	10

4	Yellow cards	2

0	Red cards	0

The measurements our data provider sent for this match. A row that was not measured shows “—”; writing 0 into an empty field would claim we measured it and found none.

When the model has said 55–60% in the past, 2,566 of 4,458 predictions were correct (57.6%). Accuracy record →

Double chance

1X — Liverpool win or draw84.9%

12 — any result other than a draw78.6%

X2 — draw or Nottingham Forest win36.5%

These three values add up to 200%, not 100% — and that is not a mistake. The options are not mutually exclusive; every outcome is counted in two of them. For the same reason 1X plus X2 exceeds 100% by exactly the probability of a draw (21.4%).

First half

Actual: 0–1

Liverpool lead at half-time40.9%

Level at half-time41.6%

Nottingham Forest lead at half-time17.5%

First-half double chance

1X — Liverpool ahead or level82.5%

12 — not level at half-time58.4%

X2 — level or Nottingham Forest ahead59.1%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1531 matches. Its edge over the base rate is markedly smaller than the full-match model's — the measured figure is on the accuracy record. Read them more cautiously. First-half goal markets are not published because they failed to beat the base rate.

Expected fouls and corners

Fouls

Liverpool: at least 10Actual: 13
Nottingham Forest: at least 10Actual: 10

Corners

Liverpool: at least 6Actual: 3
Nottingham Forest: at least 3Actual: 5

These figures are set to hold in at least 60% of matches; in the rest the count comes in below them. No upper bound is stated — we make no "at most" claim. Yellow cards were measured and are not published: there our model did not beat the historical average.

Goals market

3+ goals60.2%

When the model has said 60–65% in the past, 1,139 of 1,798 predictions were correct (63.3%). Accuracy record →

The league's historical "3+ goals" rate is 57.3%. For this match the model gives a value above that baseline — and in backtesting the model's edge was confirmed only in this direction.

What these probabilities rest on

Expected goals comes out of a team's attack weighed against its opponent's defence. A strong attack is damped when it meets a strong defence — which is why comparing the ratings on their own is misleading.

How many goals Liverpool score1.89

Attack: Liverpool score above the league average (24% more).

Opponent's defence: Nottingham Forest's defence is close to the league average (0.97×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.89 goals (including home advantage).

How many goals Nottingham Forest score0.99

Attack: Nottingham Forest score close to the league average.

Opponent's defence: Liverpool's defence is close to the league average (0.91×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 0.99 goals (including the away penalty).

Most likely scorelines

2–111.0%1–110.6%2–010.4%

A single scoreline is always less likely than an outcome: “home win” is the sum of 1-0, 2-0, 2-1 and dozens more. So the most likely single score can be a draw while the most likely outcome is a home win; the two do not contradict each other.

Ratings were computed from 1531 matches played in this league before kick-off; the team with fewer has 153 matches. No data from after the match was used. The model's track record is published unfiltered.

Week 2 of the season. This prediction was computed from previous seasons. Summer transfers, budget changes and squad rebuilds aren't in the ratings yet — the model can only see that a team has been rebuilt once new results come in.

We measured this: the bias is only pronounced in the opening week. In this week the model's overall accuracy does not separate from the rest of the season — that is, we found no measurable flaw. The one tendency across all opening weeks is that home wins are called slightly too often (said 43.0%, got 41.9%; n=583).

This sample is small and not statistically established, which is why we do not correct the model for it. The correction we tried (pulling probabilities toward the league average) didn't win on holdout, so it wasn't added. Read opening-week predictions with a little more caution.