Premier League · England · Regular Season - 2

Aston Villa–Arsenal

0–1

HT 0–0

Kick-off: Monday, 31 August 2026 at 19:00
Status: Match Finished
Venue: Villa Park, Birmingham

What the model said, and what happened

Prediction 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

59'ArsenalB. Saka (assist: R. Calafiori)

Match statistics

Match statistics
Aston Villa		Arsenal
7	Shots	7
0	Shots on target	2
3	Shots inside the box	5
1	Goalkeeper saves	0
2	Corners	4
0	Offsides	8
39%	Possession	61%
326	Passes	514
265	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Aston Villa 0.95 · Arsenal 1.64. On the pitch: 0-1.
Shots Aston Villa 7 · Arsenal 7; on target 0 and 2.
Arsenal kept the ball more (61%).

Unavailable players

Aston Villa

A. Onanaout · Knee Injury
B. Madjoout · Ankle Injury
E. Martinezout · Finger Injury
J. Manzambiout · Knee Injury
Joao Gomesout · red-card suspension
L. Baileyout · Muscle Injury
O. Watkinsout · not in squad
T. Abrahamdoubtful · injury

Arsenal

G. Martinelliout · Transfer negotiations
Gabriel Jesusout · Transfer negotiations
J. Timberout · Ankle Injury
W. Salibaout · Back Injury
Bruno Guimaraesdoubtful · Thigh Injury

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

Referee

Jarred Gillett, Australia→

3.00

yellows per match

0.00

reds per match

matches of data

1 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 63.4%: “Arsenal to win”. Second comes “a draw” at 22.2%.
Behind that distribution are the expected goals: 0.87 for Aston Villa, 1.98 for Arsenal.
The highest double chance value is 85.6% (X2 — draw or Arsenal win).
These values were computed from 1,539 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.

Aston Villa to win14.4%

Draw22.2%

Arsenal to win63.4%

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

16	Fouls	5

2	Yellow cards	1

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 50–55% in the past, 2,597 of 5,092 predictions were correct (51.0%). Accuracy record →

Double chance

1X — Aston Villa win or draw36.6%

12 — any result other than a draw77.8%

X2 — draw or Arsenal win85.6%

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 (22.2%).

First half

Actual: 0–0

Aston Villa lead at half-time14.0%

Level at half-time40.7%

Arsenal lead at half-time45.4%

First-half double chance

1X — Aston Villa ahead or level54.6%

12 — not level at half-time59.3%

X2 — level or Arsenal ahead86.0%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1539 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

Aston Villa: at least 9Actual: 16
Arsenal: at least 11Actual: 5

Corners

Aston Villa: at least 4Actual: 2
Arsenal: at least 4Actual: 4

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

The 3+ goals prediction is not published for this match.

The model gives a probability below the baseline (57.2%). In backtesting the model's edge in the "fewer goals" direction could not be confirmed; we don't publish an unconfirmed prediction.

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 Aston Villa score0.95

Attack: Aston Villa score close to the league average.

Opponent's defence: Arsenal concede 44% below the league average — a defence that seriously brakes even a strong attack.

Result: These two effects give an expectation of 0.95 goals (including home advantage).

How many goals Arsenal score1.64

Attack: Arsenal score above the league average (21% more).

Opponent's defence: Aston Villa's defence is close to the league average (1.02×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.64 goals (including the away penalty).

Most likely scorelines

0–211.6%1–111.2%1–210.9%

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 1539 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.