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Chelsea v Manchester United

Sat, Oct 31, 8:30 AM ET · Matchweek 9 · Premier League

Club research: Chelsea · Manchester United

Tested blind on past seasons. These are the model's own probability distributions, published so you can see what it says — not picks, not advice, and not compared against any price.

Model detail · how it was tested and its live record

Tested blind on nine past Premier League seasons (2013-14 to 2021-22, 3,420 matches) it was never fit on, where it beat the previous model and a plain rating system. Its total-goals numbers follow the league's recent scoring rate, so they are the same for every match — in that test they were more accurate than the previous model's team-by-team totals. Its live record this season is still being graded. 40 Premier League matches have been graded under this model — far too few to support any accuracy claim. No live win rate or accuracy figure is quoted until its season record is large enough to mean something. 36 matches were graded under the model this one replaced; that record is kept separately and is not evidence for this model.

Model-only for now: no market snapshot recent enough to compare covers this fixture yet, so there is no market comparison here — only the model's own numbers. An older captured price may still appear on a suggested card, which accepts older captures and shows the price it used. Prices join this forecast closer to kickoff, and this snapshot refreshes until then.

Simulation story

7 chapters · read from the published artifact
Skip to the report ↓
› Loading the matchup model› Reading the outcome distribution› Preparing the score scenariosPress Play to step through what the model expects, chapter by chapter, or read the report below.

Chelsea v Manchester United

Chelsea host Manchester United in matchweek 9.

model epl-model-v2-elo-poissonno run count recorded
Match result

Full time, 90 minutes

The draw is a real outcome and is never folded away.

Chelsea win38.0%
Draw25.3%
Manchester United win36.7%

Two of three outcomes

Chelsea or draw63.3%
Manchester United or draw62%
Either side wins74.7%

Each is a sum of the three probabilities above, so it cannot disagree with them.

Exact score

The ten likeliest scorelines

These ten account for 73.5% of the distribution — the remaining 26.5% is spread across every other scoreline.

Score (Chelsea–Manchester United)Probability
1–111.9%
1–08.6%
0–18.4%
2–18.4%
1–28.2%
0–06.1%
2–06.1%
0–25.8%
2–25.8%
3–14%
Goals

Total goals in the match

Expected 2.79. The middle half of the distribution falls between 2 and 4 goals.

Expected goals2.79
Median (p50)3
p10 – p901 – 5

Probability of each total (goals)

6
0
17
1
24
2
22
3
16
4
9
5
4
6
2
7
8

Over / under ladder

LineOverUnder
0.593.9%6.1%
1.576.8%23.2%
2.552.9%47.1%
3.530.7%69.3%
4.515.2%84.8%

Half lines only — a match cannot land exactly on one, so over and under sum to 100% with no push.

By side

How many each side scores

The marginal of the same grid — not a separate fit, so these add back to the total above.

Chelsea1.41 expected
24
0
34
1
24
2
11
3
4
4
1
5
Manchester United1.38 expected
25
0
35
1
24
2
11
3
4
4
1
5
Derived

Goalscoring shape and margin

Every figure is an exact sum over the same score grid.

Both teams score56.7%
Chelsea clean sheet25.1%
Manchester United clean sheet24.4%
Expected margin+0.03

Winning margin (Chelsea goals − Manchester United goals)

1
-4
4
-3
11
-2
20
-1
25
0
20
+1
11
+2
5
+3
1
+4

Positive means Chelsea win by that many, 0 is the draw, negative means Manchester United. Margins beyond ±4 carry the remaining mass and are not drawn.

Player predictions · anytime goalscorer

If he starts — likeliest scorers

The lineup is not posted yet, so each figure is the chance that player scores IF he starts — not a claim that he will. This is a different model from the score matrix above, with its own out-of-sample test.

PlayerClubAppearancesTo scoreA shot on target
Cole PalmerChelsea9133.4%65%
Bryan MbeumoManchester United12730.4%59.7%
João PedroChelsea7330.2%57.8%
Marcus RashfordManchester United7429.9%60.6%
Matheus CunhaManchester United9929.4%65.3%
Benjamin SeskoManchester United1728.9%71%
Emmanuel EmeghaChelseanone26.1%55.1%
Shea LaceyManchester Unitednone26.1%55.1%
Tynan ThompsonManchester Unitednone26.1%55.1%
Danny WelbeckChelsea9224.4%54.4%
Joshua ZirkzeeManchester United1923.5%53.5%
Jamie GittensChelsea523%48.2%

Showing the 12 likeliest of 63. No injury or suspension feed exists here, so a player who is unavailable can still appear in this list. Shots on target is a separate model from the scorer figure beside it, fitted and tested on its own — the two are not derived from one another and can disagree.

Method

Where these numbers come from

Per-club attack and defence rates are fitted to Premier League results only, then combined into an exact Poisson score matrix for this fixture. Every probability on this page is a closed-form sum over that one grid — there is no sampling here, so there is no run count to quote and the figures are identical for every visitor, every time.

The fit takes an explicit cutoff at the run clock, so it cannot see a result from a match it is forecasting. Team-level only: no lineup, injury or team-news input exists in this model, by design.

Forecast generated
Validation
Team match model tested blind on past seasons · not shown to outperform market prices
Technical detailsInternal model id and the fitted scoring rates.
Model
epl-model-v2-elo-poisson
Scoring rates (λ)
Chelsea 1.41 · Manchester United 1.38
More

Other Matchweek 9 fixtures

Paper-only and educational. Not betting advice. All Premier League fixtures