WC 2026 · Forecasting Oxford Football Forecasting
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England

UEFA Group L
9.0% Champion probability ±0.09 MC-SE
Coach
Thomas Tuchel foreign · German
Elo (model)
2,021 world 4th
Squad value
€1878M
Power → Reality
5th 4th +0.23 pp · soft draw

Fig. D1 Fixture-aware · 100k sims

England — stage progression

Round of 32: 97.53% (95% MC 97.43%–97.63%; MC-SE ±0.05 pts) Round of 32 reach 97.5% ±0.05 Round of 16: 68.93% (95% MC 68.64%–69.22%; MC-SE ±0.15 pts) Round of 16 reach 68.9% ±0.15 Quarter-final: 46.26% (95% MC 45.95%–46.57%; MC-SE ±0.16 pts) Quarter-final reach 46.3% ±0.16 Semi-final: 28.07% (95% MC 27.79%–28.35%; MC-SE ±0.14 pts) Semi-final reach 28.1% ±0.14 Final: 16.10% (95% MC 15.87%–16.32%; MC-SE ±0.12 pts) Final reach 16.1% ±0.12 Champion: 8.96% (95% MC 8.78%–9.13%; MC-SE ±0.09 pts) Champion reach 9.0% ±0.09

On the central forecast, England more likely than not reaches the Round of 16 (69%). Champion probability is 9.0% ± 0.09 pts.

Source · Oxford Football Forecasting model
Group L Confed Advance (top 2) Reach R32
1🏴󠁧󠁢󠁥󠁮󠁧󠁿EnglandUEFA68.9%97.5%
2🇭🇷CroatiaUEFA47.0%90.3%
3🇬🇭GhanaCAF6.7%36.0%
4🇵🇦PanamaCONCACAF6.7%34.8%

Source · Oxford Football Forecasting model

Bracket position Half 1 · Quadrant 2

Earliest possible meetings

Collision = the earliest round the bracket wiring could pit England against that side. Full bracket & collision matrix →

Match 22 · 2026-06-17 · Dallas Stadium home
England Croatia
47.8% win 30.5% draw 21.7% loss
Most likely 1–0 (14.8%) λ 1.32–0.80 Over 2.5 36% · BTTS 41%
Match 45 · 2026-06-23 · Boston Stadium home
England Ghana
74.5% win 19.1% draw 6.4% loss
Most likely 2–0 (17.4%) λ 2.03–0.44 Over 2.5 45% · BTTS 32%
Match 67 · 2026-06-27 · New York/New Jersey Stadium away
England Panama
78.9% win 15.8% draw 5.3% loss
Most likely 2–0 (16.3%) λ 2.35–0.48 Over 2.5 54% · BTTS 35%
How a match forecast is built

Each pairing is scored by the ensemble (Dixon-Coles bivariate-Poisson, the Bayesian hierarchical model and the global LightGBM-Poisson, log-pooled), producing an 11×11 scoreline grid that is marginalised into win/draw/loss, expected goals (λ), over/under 2.5 and both-teams-to-score. These are the same distributions the tournament simulator consumes, oriented here to England. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.

Fig. D2 Relative to the 48-team median

England vs the field

Elo rating: 2021 vs field median 1780 (1.14× the field) Elo rating 2021 med 1780 Recent NT form: 2.47 ppg vs field median 1.87 ppg (1.32× the field) Recent NT form 2.47 ppg med 1.87 ppg Squad value: €1878M vs field median €286M (6.57× the field) Squad value €1878M med €286M Squad form (global): 0.410 vs field median 0.211 (1.95× the field) Squad form (global) 0.410 med 0.211 Fitness readiness: 0.807 vs field median 0.707 (1.14× the field) Fitness readiness 0.807 med 0.707 Familiarity / chemistry: 0.055 vs field median 0.015 (3.60× the field) Familiarity / chemistry 0.055 med 0.015 Experience (mean caps): 28 vs field median 25 (1.13× the field) Experience (mean caps) 28 med 25

Read each row as a multiple of the field median: dots to the right of the dashed line are above-field, to the left below. Raw values are labelled on the right so the comparison is transparent.

Source · Oxford Football Forecasting model

Fig. D3 Bayesian projection residual g

England on the decoupling axis

aligned (0) ← record > squad price squad valued > record →

g = −0.01 ± 0.10: squad market value and recent record are closely aligned.

Source · Oxford Football Forecasting model
What g means — and its limits

g is the residual from regressing a team’s current squad market value on its history-based strength in the Bayesian hierarchical model. Positive g means the squad is valued above what the team’s record predicts; negative means the record outruns the squad’s price (the side achieves more than its market value implies). Regressed on out-of-sample success the slope is positive — squad-rich sides go a touch further — but not statistically significant at n = 3 tournaments, so treat a single team’s g as a descriptive read, not a hard prediction. The full decoupling essay →

26players
25.0mean age
28mean caps
89%in a top-5 league
16distinct clubs
4largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Jordan PickfordGKEvertonPremier League +2.21z4,4520830
2Ezri KonsaDFAston VillaPremier League +2.21z4,5572191
3Nico O'ReillyDFManchester CityPremier League +2.21z4,148940
4Declan RiceMFArsenalPremier League +2.21z4,6485726
5John StonesDFManchester CityPremier League +2.21z1,1540883
6Marc GuéhiDFManchester CityPremier League +2.21z1,6902281
7Bukayo SakaFWArsenalPremier League +2.21z3,493114814
8Elliot AndersonMFNottingham ForestPremier League +2.21z4,170480
9Harry Kane (captain)FWBayern MunichBundesliga +1.84z4,4076411379
10Jude BellinghamMFReal MadridLa Liga +2.13z3,1649476
11Marcus RashfordFWBarcelonaLa Liga +2.13z2,636147118
12Tino LivramentoDFNewcastle UnitedPremier League +2.21z1,907060
13Dean HendersonGKCrystal PalacePremier League +2.21z4,686040
14Jordan HendersonMFBrentfordPremier League +2.21z2,0941903
15Dan BurnDFNewcastle UnitedPremier League +2.21z3,561270
16Kobbie MainooMFManchester UnitedPremier League +2.21z1,9421130
17Morgan RogersMFAston VillaPremier League +2.21z4,90216141
18Anthony GordonFWNewcastle UnitedPremier League +2.21z2,96918182
19Ollie WatkinsFWAston VillaPremier League +2.21z4,12021216
20Noni MaduekeFWArsenalPremier League +2.21z2,5527101
21Eberechi EzeMFArsenalno club data163
22Ivan ToneyFWAl-AhliPro League −0.86z3,5583481
23James TraffordGKManchester CityLeague Cup +2.21z1,542020
24Reece JamesDFChelseaPremier League +2.21z3,2564231
25Djed SpenceDFTottenham HotspurPremier League +2.21z3,071050
26Jarell QuansahDFBayer LeverkusenBundesliga +1.84z3,802520

Source · Official squad announcements · API-Football (global club coverage). 1 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 96%.

Diaspora in the hosts

1,215,048

18.0 per 1,000 of home population

Host-language familiarity

Shared

primary language English · spoken in a host

Climate adaptation gap

+6.7°C

home-vs-venue heat differential

Venue extremes

45°C

peak heat index · altitude up to 177 m

Travel

6h

max time-zone shift · nearest venue 5,299 km

Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records

Fig. D4 eloratings.net method · year-end values

England — Elo since 1950

2086 world #4
England Qualified-field median

England ends the series at 2086 Elo, the world’s 4th-ranked side — above the qualified-field median.

Source · eloratings.net
Which Elo is this?

This line is the public eloratings.net series (year-end ratings), which terminates exactly at the current rating and world rank shown on the marker. It is a different number from the Elo shown in the header band (a panel-normalised rating used inside the forecast); the two are ~0.99 correlated but on different scales. We keep them distinct rather than blend them.

96% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
96% Fitness-readiness coverage Where below 100%, part of the fitness signal is imputed by the de-biasing layer.
n = 3 Out-of-sample tournaments The model is validated on three held-out World Cups; it matches the market, it does not beat it.

Validated on n=3 held-out tournaments; coverage below 1.0 means part of this squad's club-form/fitness is imputed (the global de-biasing layer). For England, 1 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →