WC 2026 · Forecasting Oxford Football Forecasting
🇮🇷

IR Iran

AFC Group G
0.4% Champion probability ±0.02 MC-SE
Coach
Amir Ghalenoei home · Iranian
Elo (model)
1,772
Squad value
€63M
Power → Reality
23rd 23rd +0.11 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

IR Iran — stage progression

Round of 32: 79.42% (95% MC 79.17%–79.67%; MC-SE ±0.13 pts) Round of 32 reach 79.4% ±0.13 Round of 16: 39.53% (95% MC 39.22%–39.83%; MC-SE ±0.15 pts) Round of 16 reach 39.5% ±0.15 Quarter-final: 14.63% (95% MC 14.41%–14.85%; MC-SE ±0.11 pts) Quarter-final reach 14.6% ±0.11 Semi-final: 4.60% (95% MC 4.47%–4.73%; MC-SE ±0.07 pts) Semi-final reach 4.6% ±0.07 Final: 1.54% (95% MC 1.47%–1.62%; MC-SE ±0.04 pts) Final reach 1.5% ±0.04 Champion: 0.45% (95% MC 0.40%–0.49%; MC-SE ±0.02 pts) Champion reach 0.4% ±0.02

On the central forecast, IR Iran more likely than not reaches the Round of 32 (79%). Champion probability is 0.4% ± 0.02 pts.

Source · Oxford Football Forecasting model
Group G Confed Advance (top 2) Reach R32
1🇧🇪BelgiumUEFA61.9%93.9%
2🇮🇷IR IranAFC39.5%79.4%
3🇪🇬EgyptCAF28.1%67.6%
4🇳🇿New ZealandOFC6.0%27.6%

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 IR Iran against that side. Full bracket & collision matrix →

Match 15 · 2026-06-16 · Los Angeles Stadium home
IR Iran New Zealand
58.1% win 27.2% draw 14.7% loss
Most likely 1–0 (16.1%) λ 1.56–0.66 Over 2.5 38% · BTTS 39%
Match 39 · 2026-06-21 · Los Angeles Stadium away
IR Iran Belgium
23.2% win 28.6% draw 48.2% loss
Most likely 1–1 (13.5%) λ 0.93–1.45 Over 2.5 42% · BTTS 47%
Match 63 · 2026-06-27 · Seattle Stadium away
IR Iran Egypt
39.8% win 32.7% draw 27.5% loss
Most likely 0–0 (14.7%) λ 1.12–0.88 Over 2.5 32% · BTTS 40%
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 IR Iran. 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

IR Iran vs the field

Elo rating: 1772 vs field median 1780 (1.00× the field) Elo rating 1772 med 1780 Recent NT form: 2.00 ppg vs field median 1.87 ppg (1.07× the field) Recent NT form 2.00 ppg med 1.87 ppg Squad value: €63M vs field median €286M (0.22× the field) Squad value €63M med €286M Squad form (global): 0.212 vs field median 0.211 (1.01× the field) Squad form (global) 0.212 med 0.211 Fitness readiness: 0.366 vs field median 0.707 (0.52× the field) Fitness readiness 0.366 med 0.707 Familiarity / chemistry: 0.061 vs field median 0.015 (3.99× the field) Familiarity / chemistry 0.061 med 0.015 Experience (mean caps): 29 vs field median 25 (1.16× the field) Experience (mean caps) 29 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

IR Iran on the decoupling axis

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

g = +0.01 ± 0.05: 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
27.2mean age
29mean caps
12%in a top-5 league
14distinct clubs
4largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Alireza BeiranvandGKTractorPersian Gulf Pro League −0.98z7720860
2Saleh HardaniDFEsteghlalAFC Champions League Two −0.05z4480181
3Ehsan Hajsafi (captain)DFSepahanno club data1467
4Shojae KhalilzadehDFTractorAFC Champions League Elite −0.05z8051582
5Milad MohammadiDFPersepolisPersian Gulf Pro League −0.98z4220761
6Saeid EzatolahiMFShabab Al Ahlino club data832
7Alireza JahanbakhshMFDenderJupiler Pro League −0.07z90719817
8Mohammad MohebiMFRostovPremier League +0.27z1,61933614
9Mehdi TaremiFWOlympiacosno club data10560
10Mehdi GhayediFWAl Nasrno club data3010
11Ali AlipourFWPersepolisno club data141
12Payam NiazmandGKPersepolisno club data150
13Hossein KanaanizadeganDFPersepolisno club data656
14Saman GhoddosMFKalbaPro League −0.09z2,2867683
15Rouzbeh CheshmiMFEsteghlalAFC Champions League Two −0.05z900403
16Mehdi TorabiMFTractorPersian Gulf Pro League −0.98z3141527
17Aria YousefiDFSepahanPersian Gulf Pro League −0.98z4051141
18Amirhossein HosseinzadehFWTractorPersian Gulf Pro League −0.98z2,3639185
19Ali NematiDFFooladno club data170
20Shahriyar MoghanlouFWKalbaPro League −0.09z2,51213212
21Mohammad GhorbaniMFAl Wahdano club data160
22Hossein HosseiniGKSepahanPersian Gulf Pro League −0.98z4500130
23Ramin RezaeianDFFooladPersian Gulf Pro League −0.98z00748
24Dennis EckertFWStandard LiègeJupiler Pro League −0.07z1,987500
25Danial EiriDFMalavanno club data00
26Amirmohammad RazzaghiniaMFEsteghlalPersian Gulf Pro League −0.98z443040

Source · Official squad announcements · API-Football (global club coverage). 10 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 58%.

Diaspora in the hosts

553,852

6.0 per 1,000 of home population

Host-language familiarity

Foreign

primary language Persian (Farsi)

Climate adaptation gap

−0.7°C

home-vs-venue heat differential

Venue extremes

29°C

peak heat index · altitude up to 45 m

Travel

11h

max time-zone shift · nearest venue 9,589 km

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

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

IR Iran — Elo since 1950

1897 world #26
IR Iran Qualified-field median

IR Iran ends the series at 1897 Elo, the world’s 26th-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.

58% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
62% 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 IR Iran, 10 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →