Fixture 40scheduledGW 4

Leeds v Newcastle

Mon 14 Sept 19:00Zmarket prices held

Expected goals1.35 — 1.62

Expected points1.19 — 1.55

Over 2.556.8%fair 1.76

Both score59.9%fair 1.67

Most likely1-111.9%

Markets priced27

Ch 01 · Outcome

Match result

The bar is the model's probability; the caret on the same scale is the market's, with the margin removed. The distance between them is the disagreement.

OutcomeModel fill · market caretProbFairΔ mkt

LEE

31.2%3.20−7.0 pp

Draw

25.6%3.91−1.1 pp

NEW

43.2%2.31+8.1 pp

Ch 02 · Book

27 markets

dixon_coles xi001-20260903

Model fair prices, computed as 1 / probability from a Dixon-Coles score matrix. No bookmaker margin is included, and no bookmaker is quoting these. A real price will be shorter than every number here.

Match result

Leeds31.2%3.20

Draw25.6%3.91

Newcastle43.2%2.31

Double chance

Leeds or draw56.8%1.76

Leeds or Newcastle74.4%1.34

Draw or Newcastle68.8%1.45

Draw no bet

Leeds42.0%2.38

Newcastle58.0%1.72

A draw returns the stake, so these prices exclude it.

Win to nil

Leeds14.0%7.13

Newcastle20.2%4.95

Clean sheet

Leeds19.9%5.04

Newcastle26.1%3.84

Ch 03 · Reasoning

What is driving this

computed, not generated

Computed arithmetic, not generated text. Each driver is a stored quantity: the Elo difference replayed from results, points per game over the last five matches, rolling expected goals, and each absent player's share of team expected goals.

Home advantage+60 Elo equivalent

Favours LEE. the football default is around 100 Elo points, but Premier League home advantage fell during the crowdless 2020/21 season and settled lower afterwards, so the ratings carry a smaller term

Team rating gap-48 Elo

Favours NEW. LEE 1506 against NEW 1555, computed from stored results; an Elo-driven model scores RPS around 0.2015 on this league against roughly 0.194 for de-vigged closing odds

Underlying expected goals-0.15 net xG per match

Favours NEW. LEE -0.51, NEW -0.35 across the form window — results are noisier than the chances behind them

Unavailable players1% home against 3% away xG missing

Favours LEE. 1 out and 2 doubtful for LEE, 4 out and 0 doubtful for NEW; measured as the share of recent expected goals those players contributed

Recent form1.60 against 1.60 points per game

last 5 and 5 completed matches: DWLWD against WDLWD

Ch 04 · Comparison

Both sides

LEE

DWLWD

11 May – 30 Aug

NEW

DWLDW

10 May – 29 Aug

1.60Points per game1.60

4Goals for8

5Goals against6

6.18Expected goals6.63

1,506Elo1,555

Head to head over 6 meetings in the seasons held (Premier League from 2021-22, Championship from 2024-25): LEE 0, drawn 4, NEW 2. Cup ties and earlier seasons are not included, so a full-history site will show more.

Ch 05 · Market

Bookmaker prices

Real bookmaker prices, collected from published market data.

Market	Selection	Best	Book	Implied	N
h2h	away	2.82	1xBet	35.5%	31
h2h	draw	3.64	1xBet	27.5%	31
h2h	home	2.59	1xBet	38.6%	31

Ch 06 · Availability

Leeds

0.8% xG out

Share of attacking output is what moves the number, so it is shown rather than implied.

Mateo JosephFWDinjured0%

Knee injury - Unknown return date

RodonDEFdoubtful50%1.6% xG

Hamstring injury - 50% chance of playing

GruevMIDdoubtful25%

Knee injury - 25% chance of playing

Ch 06 · Availability

Newcastle

2.6% xG out

Share of attacking output is what moves the number, so it is shown rather than implied.

OsulaFWDinjured0%2.6% xG

Foot injury - Unknown return date

BurnDEFinjured0%

Ankle injury - Expected back 14 Sep

JoelintonMIDinjured0%

Unspecified injury - Unknown return date

LivramentoDEFinjured0%

Calf injury - Unknown return date

Ch 07 · Simulation

Re-price without a player

Removing a player scales that side's expected goals by their share of recent attacking output, then rebuilds the score matrix. Every market moves together.

Ch 09 · Divergence

Where the model differs from a price

Shown with the model's own record, because that is what decides how much these are worth.

h2h awaymodel 43.2% vs 2.82 (1xBet)+21.8 ppstake 2.0%medium confidence

Ch 10 · Calibration

How this model actually scores

Dixon-Coles bivariate Poisson fitted to Premier League results with exponential time decay. Statistical model, no language model involved.

loses to closing market2,281 out-of-sample fixtures

Metric	Model	Market	Δ
Ranked probability score	0.2047	0.1965	+0.0082
Log loss	0.9882	0.9623	+0.0259
Brier	0.5883	0.5711	+0.0172
Accuracy	52.5%	54.8%	−2.3 pp

Lower is better for the first three. These probabilities are for reading a fixture, not for expecting profit against closing prices. What these numbers mean