WC 2026 · Forecasting Oxford Football Forecasting
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Scotland

UEFA Group C
0.2% Champion probability ±0.01 MC-SE
Coach
Steve Clarke
Elo (model)
1,782 world 33rd
Squad value
€272M
Power → Reality
29th 29th +0.01 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Scotland — stage progression

Round of 32: 70.41% (95% MC 70.12%–70.69%; MC-SE ±0.14 pts) Round of 32 reach 70.4% ±0.14 Round of 16: 25.51% (95% MC 25.24%–25.78%; MC-SE ±0.14 pts) Round of 16 reach 25.5% ±0.14 Quarter-final: 9.55% (95% MC 9.37%–9.74%; MC-SE ±0.09 pts) Quarter-final reach 9.6% ±0.09 Semi-final: 3.04% (95% MC 2.94%–3.15%; MC-SE ±0.05 pts) Semi-final reach 3.0% ±0.05 Final: 0.86% (95% MC 0.81%–0.92%; MC-SE ±0.03 pts) Final reach 0.9% ±0.03 Champion: 0.22% (95% MC 0.19%–0.25%; MC-SE ±0.01 pts) Champion reach 0.2% ±0.01

On the central forecast, Scotland more likely than not reaches the Round of 32 (70%). Champion probability is 0.2% ± 0.01 pts.

Source · Oxford Football Forecasting model
Group C Confed Advance (top 2) Reach R32
1🇧🇷BrazilCONMEBOL67.1%98.3%
2🇲🇦MoroccoCAF44.9%88.8%
3🏴󠁧󠁢󠁳󠁣󠁴󠁿ScotlandUEFA25.5%70.4%
4🇭🇹HaitiCONCACAF1.4%11.6%

Source · Oxford Football Forecasting model

Bracket position Half 1 · Quadrant 2

Earliest possible meetings

No collision rows recorded for this team.

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

Match 5 · 2026-06-14 · Boston Stadium away
Scotland Haiti
63.6% win 23.7% draw 12.7% loss
Most likely 1–0 (14.1%) λ 1.81–0.69 Over 2.5 46% · BTTS 42%
Match 30 · 2026-06-19 · Boston Stadium home
Scotland Morocco
21.3% win 32.8% draw 45.9% loss
Most likely 0–1 (16.8%) λ 0.71–1.18 Over 2.5 29% · BTTS 36%
Match 49 · 2026-06-24 · Miami Stadium home
Scotland Brazil
12.7% win 22.9% draw 64.4% loss
Most likely 0–2 (13.1%) λ 0.72–1.89 Over 2.5 48% · BTTS 44%
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 Scotland. 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

Scotland vs the field

Elo rating: 1782 vs field median 1780 (1.00× the field) Elo rating 1782 med 1780 Recent NT form: 1.87 ppg vs field median 1.87 ppg (1.00× the field) Recent NT form 1.87 ppg med 1.87 ppg Squad value: €272M vs field median €286M (0.95× the field) Squad value €272M med €286M Squad form (global): 0.322 vs field median 0.211 (1.53× the field) Squad form (global) 0.322 med 0.211 Fitness readiness: 0.589 vs field median 0.707 (0.83× the field) Fitness readiness 0.589 med 0.707 Familiarity / chemistry: 0.012 vs field median 0.015 (0.80× the field) Familiarity / chemistry 0.012 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

Scotland on the decoupling axis

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

g = +0.30 ± 0.05: 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
26.0mean age
29mean caps
54%in a top-5 league
22distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Angus GunnGKNottingham ForestPremier League +2.21z450220
2Aaron HickeyDFBrentfordPremier League +2.21z1,0591210
3Andy Robertson (captain)DFLiverpoolPremier League +2.21z2,1094944
4Scott McTominayMFNapoliSerie A +1.70z3,706147015
5Grant HanleyDFHibernianPremiership −0.28z2,0120682
6Kieran TierneyDFCelticPremiership −0.28z3,3816562
7John McGinnMFAston VillaPremier League +2.21z3,118108620
8Tyler FletcherMFManchester UnitedPremier League +2.21z107020
9Lyndon DykesFWCharlton AthleticChampionship +2.21z1,52435110
10Ché AdamsFWTorinoSerie A +1.70z2,11584713
11Ryan ChristieMFBournemouthPremier League +2.21z1,15026810
12Liam KellyGKRangersno club data30
13Jack HendryDFAl-Ettifaqno club data383
14Ross StewartFWSouthamptonChampionship +2.21z1,3521130
15John SouttarDFRangersPremiership −0.28z3,6441242
16Dominic HyamDFWrexhamChampionship +2.21z3,920140
17Ben Gannon-DoakFWBournemouthPremier League +2.21z1670141
18George HirstFWIpswich TownChampionship +2.21z2,22211101
19Lewis FergusonMFBolognaSerie A +1.70z2,9491241
20Lawrence ShanklandFWHeart of MidlothianLeague Cup −0.28z5,53340207
21Craig GordonGKHeart of MidlothianPremiership −0.28z2260840
22Nathan PattersonDFEvertonPremier League +2.21z3820261
23Kenny McLeanMFNorwich Cityno club data583
24Anthony RalstonDFCelticPremiership −0.28z1,6380271
25Findlay CurtisFWKilmarnockPremiership −0.28z1,076531
26Scott McKennaDFDinamo ZagrebHNL +0.34z3,5482501

Source · Official squad announcements · API-Football (global club coverage). 3 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 88%.

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

+3.7°C

home-vs-venue heat differential

Venue extremes

42°C

peak heat index · altitude up to 83 m

Travel

5h

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

Scotland — Elo since 1950

1844 world #33
Scotland Qualified-field median

Scotland ends the series at 1844 Elo, the world’s 33rd-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.

88% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
88% 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 Scotland, 3 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →