WC 2026 · Forecasting Oxford Football Forecasting
🇹🇷

Türkiye

UEFA Group D
1.0% Champion probability ±0.03 MC-SE
Coach
Vincenzo Montella foreign · Italian
Elo (model)
1,911
Squad value
€413M
Power → Reality
19th 19th +0.07 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Türkiye — stage progression

Round of 32: 76.75% (95% MC 76.49%–77.01%; MC-SE ±0.13 pts) Round of 32 reach 76.7% ±0.13 Round of 16: 43.73% (95% MC 43.42%–44.04%; MC-SE ±0.16 pts) Round of 16 reach 43.7% ±0.16 Quarter-final: 19.43% (95% MC 19.19%–19.68%; MC-SE ±0.13 pts) Quarter-final reach 19.4% ±0.13 Semi-final: 7.29% (95% MC 7.12%–7.45%; MC-SE ±0.08 pts) Semi-final reach 7.3% ±0.08 Final: 2.87% (95% MC 2.77%–2.98%; MC-SE ±0.05 pts) Final reach 2.9% ±0.05 Champion: 1.03% (95% MC 0.96%–1.09%; MC-SE ±0.03 pts) Champion reach 1.0% ±0.03

On the central forecast, Türkiye more likely than not reaches the Round of 32 (77%). Champion probability is 1.0% ± 0.03 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 Türkiye against that side. Full bracket & collision matrix →

Match 6 · 2026-06-14 · BC Place Vancouver away
Türkiye Australia
40.6% win 30.6% draw 28.8% loss
Most likely 1–1 (14.2%) λ 1.24–1.00 Over 2.5 39% · BTTS 46%
Match 31 · 2026-06-20 · San Francisco Bay Area Stadium home
Türkiye Paraguay
37.7% win 31.3% draw 31% loss
Most likely 1–1 (14.4%) λ 1.16–1.02 Over 2.5 37% · BTTS 45%
Match 59 · 2026-06-26 · Los Angeles Stadium home
Türkiye USA
43.6% win 28.3% draw 28.1% loss
Most likely 1–1 (13.5%) λ 1.44–1.11 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 Türkiye. 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

Türkiye vs the field

Elo rating: 1911 vs field median 1780 (1.07× the field) Elo rating 1911 med 1780 Recent NT form: 2.27 ppg vs field median 1.87 ppg (1.21× the field) Recent NT form 2.27 ppg med 1.87 ppg Squad value: €413M vs field median €286M (1.45× the field) Squad value €413M med €286M Squad form (global): 0.254 vs field median 0.211 (1.20× the field) Squad form (global) 0.254 med 0.211 Fitness readiness: 0.704 vs field median 0.707 (1.00× the field) Fitness readiness 0.704 med 0.707 Familiarity / chemistry: 0.092 vs field median 0.015 (5.99× the field) Familiarity / chemistry 0.092 med 0.015 Experience (mean caps): 28 vs field median 25 (1.12× 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

Türkiye on the decoupling axis

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

g = −0.41 ± 0.07: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.

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
25.1mean age
28mean caps
31%in a top-5 league
16distinct clubs
6largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Mert GünokGKFenerbahçeSüper Lig +0.49z3850370
2Zeki ÇelikDFRomaSerie A +1.70z5,1601613
3Merih DemiralDFAl-AhliPro League −0.86z1,9592626
4Çağlar SöyüncüDFFenerbahçeSüper Lig +0.49z1,0580602
5Salih ÖzcanMFBorussia DortmundBundesliga +1.84z940301
6Orkun KökçüMFBeşiktaşSüper Lig +0.49z3,0009504
7Kerem AktürkoğluFWFenerbahçeSüper Lig +0.49z2,949155215
8Arda GülerFWReal MadridLa Liga +2.13z3,6847296
9Deniz GülFWPortono club data82
10Hakan Çalhanoğlu (captain)MFInter MilanSerie A +1.70z2,3101210522
11Kenan YıldızFWJuventusno club data285
12Altay BayındırGKManchester Unitedno club data120
13Eren ElmalıDFGalatasaraySüper Lig +0.49z1,9084230
14Abdülkerim BardakcıDFGalatasaraySüper Lig +0.49z3,5141272
15Ozan KabakDFTSG HoffenheimBundesliga +1.84z1,7984302
16İsmail YüksekMFFenerbahçeSüper Lig +0.49z2,4172321
17İrfan Can KahveciFWKasımpaşaSüper Lig +0.49z1,3712476
18Mert MüldürDFFenerbahçeSüper Lig +0.49z2,2101453
19Yunus AkgünFWGalatasaraySüper Lig +0.49z2,30410194
20Ferdi KadıoğluDFBrighton & Hove AlbionPremier League +2.21z3,5161302
21Barış Alper YılmazFWGalatasaraySüper Lig +0.49z3,43110354
22Kaan AyhanMFGalatasaraySüper Lig +0.49z5581735
23Uğurcan ÇakırGKGalatasarayno club data390
24Oğuz AydınFWFenerbahçeno club data110
25Samet AkaydinDFÇaykur RizesporSüper Lig +0.49z2,5714191
26Can UzunFWEintracht FrankfurtBundesliga +1.84z1,5221061

Source · Official squad announcements · API-Football (global club coverage). 5 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 81%.

Diaspora in the hosts

142,014

2.0 per 1,000 of home population

Host-language familiarity

Foreign

primary language Turkish

Climate adaptation gap

−4.3°C

home-vs-venue heat differential

Venue extremes

29°C

peak heat index · altitude up to 45 m

Travel

10h

max time-zone shift · nearest venue 8,125 km

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

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

Türkiye — Elo since 1950

1969 world #16
Türkiye Qualified-field median

Türkiye ends the series at 1969 Elo, the world’s 16th-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.

81% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
81% 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 Türkiye, 5 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →