WC 2026 · Forecasting Oxford Football Forecasting
🇺🇸

USA

CONCACAF Group D Host nation
0.2% Champion probability ±0.01 MC-SE
Coach
Mauricio Pochettino foreign · Argentine
Elo (model)
1,726
Squad value
€452M
Power → Reality
31st 31st +0.01 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

USA — stage progression

Round of 32: 67.20% (95% MC 66.91%–67.49%; MC-SE ±0.15 pts) Round of 32 reach 67.2% ±0.15 Round of 16: 29.44% (95% MC 29.16%–29.72%; MC-SE ±0.14 pts) Round of 16 reach 29.4% ±0.14 Quarter-final: 9.44% (95% MC 9.26%–9.62%; MC-SE ±0.09 pts) Quarter-final reach 9.4% ±0.09 Semi-final: 2.57% (95% MC 2.47%–2.67%; MC-SE ±0.05 pts) Semi-final reach 2.6% ±0.05 Final: 0.71% (95% MC 0.66%–0.76%; MC-SE ±0.03 pts) Final reach 0.7% ±0.03 Champion: 0.17% (95% MC 0.14%–0.19%; MC-SE ±0.01 pts) Champion reach 0.2% ±0.01

On the central forecast, USA more likely than not reaches the Round of 32 (67%). Champion probability is 0.2% ± 0.01 pts.

Source · Oxford Football Forecasting model
Group D Confed Advance (top 2) Reach R32
1🇹🇷TürkiyeUEFA43.7%76.7%
2🇵🇾ParaguayCONMEBOL35.6%69.2%
3🇺🇸USACONCACAF29.4%67.2%
4🇦🇺AustraliaAFC28.6%61.3%

Source · Oxford Football Forecasting model

Bracket position Half 0 · Quadrant 1

Earliest possible meetings

No collision rows recorded for this team.

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

Match 4 · 2026-06-13 · Los Angeles Stadium home
USA Paraguay
35.5% win 29.7% draw 34.8% loss
Most likely 1–1 (14.1%) λ 1.21–1.20 Over 2.5 43% · BTTS 50%
Match 32 · 2026-06-19 · Seattle Stadium home
USA Australia
38.6% win 29.1% draw 32.3% loss
Most likely 1–1 (13.9%) λ 1.31–1.17 Over 2.5 45% · BTTS 51%
Match 59 · 2026-06-26 · Los Angeles Stadium away
USA Türkiye
28.1% win 28.3% draw 43.6% loss
Most likely 1–1 (13.5%) λ 1.11–1.44 Over 2.5 47% · BTTS 52%
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 USA. 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

USA vs the field

Elo rating: 1726 vs field median 1780 (0.97× the field) Elo rating 1726 med 1780 Recent NT form: 1.73 ppg vs field median 1.87 ppg (0.93× the field) Recent NT form 1.73 ppg med 1.87 ppg Squad value: €452M vs field median €286M (1.58× the field) Squad value €452M med €286M Squad form (global): 0.236 vs field median 0.211 (1.12× the field) Squad form (global) 0.236 med 0.211 Fitness readiness: 0.805 vs field median 0.707 (1.14× the field) Fitness readiness 0.805 med 0.707 Familiarity / chemistry: 0.006 vs field median 0.015 (0.40× the field) Familiarity / chemistry 0.006 med 0.015 Experience (mean caps): 23 vs field median 25 (0.93× the field) Experience (mean caps) 23 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

USA on the decoupling axis

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

g = +0.43 ± 0.06: the squad is valued above its record — the transfer market rates this side above what its results have earned.

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
24.2mean age
23mean caps
62%in a top-5 league
24distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Matt TurnerGKNew England RevolutionMajor League Soccer −0.71z9000540
2Sergiño DestDFPSV EindhovenEredivisie +0.74z2,9191393
3Chris RichardsDFCrystal PalacePremier League +2.21z4,2272363
4Tyler AdamsMFBournemouthPremier League +2.21z2,0692542
5Antonee RobinsonDFFulhamPremier League +2.21z1,7961545
6Auston TrustyDFCelticLeague Cup −0.28z5,426280
7Giovanni ReynaMFBorussia MönchengladbachBundesliga +1.84z5941389
8Weston McKennieMFJuventusSerie A +1.70z4,15496612
9Ricardo PepiFWPSV EindhovenEredivisie +0.74z1,775193713
10Christian PulisicFWMilanno club data8633
11Brenden AaronsonFWLeeds UnitedPremier League +2.21z2,7544589
12Miles RobinsonDFFC CincinnatiMajor League Soccer −0.71z1,5961403
13Tim Ream (captain)DFCharlotte FCMajor League Soccer −0.71z4,0811821
14Sebastian BerhalterMFVancouver Whitecaps FCMajor League Soccer −0.71z3,4877131
15Cristian RoldanMFSeattle Sounders FCMajor League Soccer −0.71z3,1681470
16Alex FreemanDFVillarrealLa Liga +2.13z3560172
17Malik TillmanMFBayer LeverkusenBundesliga +1.84z2,4028303
18Max ArfstenDFColumbus CrewMajor League Soccer −0.71z3,2457201
19Haji WrightFWCoventry CityChampionship +2.21z2,75518207
20Folarin BalogunFWMonacoLigue 1 +1.70z3,31419279
21Timothy WeahFWMarseilleLigue 1 +1.70z3,1393517
22Mark McKenzieDFToulouseLigue 1 +1.70z2,7820290
23Joe ScallyDFBorussia MönchengladbachBundesliga +1.84z2,7922260
24Matt FreeseGKNew York City FCMajor League Soccer −0.71z3,4160150
25Chris BradyGKChicago Fire FCMajor League Soccer −0.71z2,655010
26Alejandro ZendejasFWAméricaLiga MX +0.22z2,81914142

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

1,072,474

3.0 per 1,000 of home population

Host-language familiarity

Shared

primary language English · spoken in a host

Climate adaptation gap

−1.2°C

home-vs-venue heat differential

Venue extremes

29°C

peak heat index · altitude up to 45 m

Travel

3h

max time-zone shift · nearest venue 197 km

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

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

USA — Elo since 1950

1827 world #36
USA Qualified-field median

USA ends the series at 1827 Elo, the world’s 36th-ranked side — below 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 USA, 1 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →