Qatar v Switzerland · Oxford Football Forecasting

🇶🇦 Qatar AFC Elo 1,421 · world 115th

5 / 14 / 81 win · draw · win most likely 0–2

🇨🇭 Switzerland UEFA Elo 1,891 · world 19th

Group: B
Date: Saturday 13 June 2026
Kick-off: 19:00 UTC
Venue: San Francisco Bay Area Stadium, Santa Clara

§ 01

The scoreline grid

The ensemble's full distribution over exact scorelines — Qatar goals down the side, Switzerland goals across the top. This is the same 11×11 grid the tournament simulator samples.

Fig. V7 Ensemble · Group B

Qatar v Switzerland — scoreline probabilities

Cells show P(exact scoreline); the right column and bottom row are the marginal totals P(Qatar scores k) and P(Switzerland scores k). Grid runs 0–7 goals per side; the 8–10-goal tail holds 0.45% of the mass and is omitted from the cells (not from the totals).

The grid makes Switzerland favourites at 80.5%, with a 14.4% draw. The single most-likely scoreline is 0–2 (15.1%), but no exact score clears 15% — the distribution is broad, as it should be.

Source · Oxford Football Forecasting model

§ 02

Result, goals & totals

The grid marginalised into the outcome split, expected goals each side, and the goal-total markets — every figure read straight off the same distribution.

Win · draw · loss

14.4% 80.5%

🇶🇦 Qatar 5.1% Draw 14.4% 🇨🇭 Switzerland 80.5%

Rounded values sum to exactly 100%.

Expected goals (λ)

🇶🇦Qatar 0.53

🇨🇭Switzerland 2.53

Poisson means feeding the grid; combined expected goals 3.06.

59.0% Over 2.5 goals P(3 or more goals in the match)

41.0% Under 2.5 goals complement of over-2.5

38.1% Both teams to score P(each side scores ≥ 1)

0–2 Most-likely scoreline modal exact score · 15.1%

§ 03

Venue context

The conditions at San Francisco Bay Area Stadium, Santa Clara — the same environment layer the dossiers' climate exposure draws on.

Levi's Santa Clara, USA

Heat index 28°C apparent temperature (June–July)

Max temperature 27°C June–July daily high

Humidity 63% relative humidity

Altitude 1m above sea level

Source · Open-Meteo & venue records. Travel and time-zone exposure are per-team — see each side's dossier.

§ 04

Why the model leans this way

The inputs behind the grid — each side's strength signals laid side by side. The forecast is the ensemble's, not a hand-weighting of these; this panel is the descriptive read.

1,421 Elo rating 1,891

0.93 Recent NT form 1.93

€30M Squad value €336M

0.052 Squad form (global) 0.210

0.330 Fitness readiness 0.804

+0.74 Decoupling g +0.21

Switzerland carry the Elo edge (470 points). On the decoupling axis, Qatar is the side whose squad is valued higher relative to its record.

How a single-match forecast is built

The pairing is scored by the ensemble — Dixon-Coles bivariate-Poisson, the Bayesian hierarchical model and the global LightGBM-Poisson, log-pooled — yielding the 11×11 scoreline grid above. Win/draw/loss, expected goals (λ), over-2.5 and both-teams-to-score are all marginals of that one grid, so they are mutually consistent by construction. The strength inputs shown here feed the models; the forecast is their pooled output, not a manual weighting of these rows. The model matches the market out-of-sample (RPS 0.1891 vs 0.1905); it does not significantly beat it at n = 3. The ensemble, in full →