WC 2026 · Forecasting Oxford Football Forecasting
🇵🇹

Portugal

UEFA Group K
6.2% Champion probability ±0.08 MC-SE
Coach
Roberto Martínez
Elo (model)
1,986 world 7th
Squad value
€1122M
Power → Reality
6th 6th +0.14 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Portugal — stage progression

Round of 32: 93.82% (95% MC 93.67%–93.97%; MC-SE ±0.08 pts) Round of 32 reach 93.8% ±0.08 Round of 16: 64.40% (95% MC 64.10%–64.69%; MC-SE ±0.15 pts) Round of 16 reach 64.4% ±0.15 Quarter-final: 39.62% (95% MC 39.31%–39.92%; MC-SE ±0.15 pts) Quarter-final reach 39.6% ±0.15 Semi-final: 22.35% (95% MC 22.09%–22.60%; MC-SE ±0.13 pts) Semi-final reach 22.3% ±0.13 Final: 12.14% (95% MC 11.94%–12.34%; MC-SE ±0.10 pts) Final reach 12.1% ±0.10 Champion: 6.16% (95% MC 6.01%–6.30%; MC-SE ±0.08 pts) Champion reach 6.2% ±0.08

On the central forecast, Portugal more likely than not reaches the Round of 16 (64%). Champion probability is 6.2% ± 0.08 pts.

Source · Oxford Football Forecasting model
Group K Confed Advance (top 2) Reach R32
1🇵🇹PortugalUEFA64.4%93.8%
2🇨🇴ColombiaCONMEBOL57.9%91.4%
3🇨🇩Congo DRCAF10.8%40.2%
4🇺🇿UzbekistanAFC8.4%36.4%

Source · Oxford Football Forecasting model

Bracket position Half 1 · Quadrant 3

Earliest possible meetings

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

Match 23 · 2026-06-17 · Houston Stadium home
Portugal Congo DR
65.4% win 24.2% draw 10.4% loss
Most likely 1–0 (17.2%) λ 1.72–0.54 Over 2.5 39% · BTTS 35%
Match 47 · 2026-06-23 · Houston Stadium home
Portugal Uzbekistan
70.4% win 20.7% draw 8.9% loss
Most likely 2–0 (15.3%) λ 1.99–0.57 Over 2.5 47% · BTTS 38%
Match 71 · 2026-06-27 · Miami Stadium away
Portugal Colombia
37.1% win 31% draw 31.9% loss
Most likely 1–1 (14.4%) λ 1.17–1.06 Over 2.5 39% · BTTS 46%
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 Portugal. 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

Portugal vs the field

Elo rating: 1986 vs field median 1780 (1.12× the field) Elo rating 1986 med 1780 Recent NT form: 2.07 ppg vs field median 1.87 ppg (1.11× the field) Recent NT form 2.07 ppg med 1.87 ppg Squad value: €1122M vs field median €286M (3.93× the field) Squad value €1122M med €286M Squad form (global): 0.266 vs field median 0.211 (1.26× the field) Squad form (global) 0.266 med 0.211 Fitness readiness: 0.792 vs field median 0.707 (1.12× the field) Fitness readiness 0.792 med 0.707 Familiarity / chemistry: 0.043 vs field median 0.015 (2.80× the field) Familiarity / chemistry 0.043 med 0.015 Experience (mean caps): 42 vs field median 25 (1.68× the field) Experience (mean caps) 42 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

Portugal on the decoupling axis

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

g = +0.75 ± 0.07: 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
25.2mean age
42mean caps
81%in a top-5 league
17distinct clubs
4largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Diogo CostaGKPortoPrimeira Liga +1.14z7,1600420
2Nélson SemedoDFFenerbahçeSüper Lig +0.49z3,0211490
3Rúben DiasDFManchester CityPremier League +2.21z3,0872753
4Tomás AraújoDFBenficaPrimeira Liga +1.14z2,618140
5Diogo DalotDFManchester UnitedPremier League +2.21z2,7971343
6Matheus NunesMFManchester CityPremier League +2.21z4,3031192
7Cristiano Ronaldo (captain)FWAl-NassrPro League −0.86z4,74450227143
8Bruno FernandesMFManchester UnitedPremier League +2.21z4,101138829
9Gonçalo RamosFWParis Saint-GermainLigue 1 +1.70z1,730122410
10Bernardo SilvaMFManchester CityPremier League +2.21z4,254510814
11João FélixFWAl-NassrPro League −0.86z3,274245312
12José SáGKWolverhampton Wanderersno club data50
13Renato VeigaDFVillarrealLa Liga +2.13z3,3591121
14Gonçalo InácioDFSporting CPPrimeira Liga +1.14z3,8023212
15João NevesMFParis Saint-GermainLigue 1 +1.70z3,1289213
16Francisco TrincãoFWSporting CPPrimeira Liga +1.14z4,16711173
17Rafael LeãoFWMilanno club data445
18Pedro NetoFWChelseaPremier League +2.21z4,83716242
19Gonçalo GuedesFWReal SociedadLa Liga +2.13z2,3219348
20João CanceloDFBarcelonaLa Liga +2.13z1,44826712
21Rúben NevesMFAl-HilalPro League −0.86z6,59015661
22Rui SilvaGKSporting CPPrimeira Liga +1.14z4,215030
23VitinhaMFParis Saint-GermainLigue 1 +1.70z4,5127370
24Samú CostaDFMallorcaLa Liga +2.13z2,808750
25Nuno MendesDFParis Saint-GermainLigue 1 +1.70z2,9946431
26Francisco ConceiçãoFWJuventusSerie A +1.70z2,6944163

Source · Official squad announcements · API-Football (global club coverage). 2 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 92%.

Diaspora in the hosts

342,103

32.0 per 1,000 of home population

Host-language familiarity

Foreign

primary language Portuguese

Climate adaptation gap

+4.9°C

home-vs-venue heat differential

Venue extremes

47°C

peak heat index · altitude up to 15 m

Travel

5h

max time-zone shift · nearest venue 5,157 km

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

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

Portugal — Elo since 1950

2042 world #7
Portugal Qualified-field median

Portugal ends the series at 2042 Elo, the world’s 7th-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.

92% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
92% 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 Portugal, 2 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →