WC 2026 · Forecasting Oxford Football Forecasting
🇳🇿

New Zealand

OFC Group G
0.0% Champion probability ±0.00 MC-SE
Coach
Darren Bazeley foreign · English
Elo (model)
1,562 world 62nd
Squad value
€28M
Power → Reality
44th 46th −0.00 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

New Zealand — stage progression

Round of 32: 27.59% (95% MC 27.31%–27.86%; MC-SE ±0.14 pts) Round of 32 reach 27.6% ±0.14 Round of 16: 6.05% (95% MC 5.90%–6.20%; MC-SE ±0.08 pts) Round of 16 reach 6.0% ±0.08 Quarter-final: 0.84% (95% MC 0.79%–0.90%; MC-SE ±0.03 pts) Quarter-final reach 0.8% ±0.03 Semi-final: 0.08% (95% MC 0.06%–0.10%; MC-SE ±0.01 pts) Semi-final reach 0.1% ±0.01 Final: 0.01% (95% MC 0.00%–0.01%; MC-SE ±0.00 pts) Final reach 0.0% ±0.00 Champion: 0.00% (95% MC 0.00%–0.00%; MC-SE ±0.00 pts) Champion reach 0.0% ±0.00

New Zealand is most likely eliminated before the knockout rounds: 28% to clear the group. Champion probability 0.00%.

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 New Zealand against that side. Full bracket & collision matrix →

Match 15 · 2026-06-16 · Los Angeles Stadium away
New Zealand IR Iran
14.7% win 27.2% draw 58.1% loss
Most likely 0–1 (16.1%) λ 0.66–1.56 Over 2.5 38% · BTTS 39%
Match 40 · 2026-06-22 · BC Place Vancouver home
New Zealand Egypt
18.6% win 31.3% draw 50.1% loss
Most likely 0–1 (17.1%) λ 0.67–1.28 Over 2.5 31% · BTTS 36%
Match 64 · 2026-06-27 · BC Place Vancouver home
New Zealand Belgium
7.1% win 17.4% draw 75.5% loss
Most likely 0–2 (15.1%) λ 0.57–2.27 Over 2.5 54% · BTTS 39%
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 New Zealand. 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

New Zealand vs the field

Elo rating: 1562 vs field median 1780 (0.88× the field) Elo rating 1562 med 1780 Recent NT form: 1.07 ppg vs field median 1.87 ppg (0.57× the field) Recent NT form 1.07 ppg med 1.87 ppg Squad value: €28M vs field median €286M (0.10× the field) Squad value €28M med €286M Squad form (global): 0.127 vs field median 0.211 (0.60× the field) Squad form (global) 0.127 med 0.211 Fitness readiness: 0.690 vs field median 0.707 (0.98× the field) Fitness readiness 0.690 med 0.707 Familiarity / chemistry: 0.040 vs field median 0.015 (2.60× the field) Familiarity / chemistry 0.040 med 0.015 Experience (mean caps): 27 vs field median 25 (1.11× the field) Experience (mean caps) 27 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

New Zealand on the decoupling axis

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

g = +0.01 ± 0.11: 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
25.8mean age
27mean caps
8%in a top-5 league
20distinct clubs
5largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Max CrocombeGKMillwallChampionship +2.21z2,2550240
2Tim PayneDFWellington PhoenixA-League −2.17z8680513
3Francis de VriesDFAuckland FCA-League −2.17z2,0221201
4Tyler BindonDFSheffield Unitedno club data253
5Michael BoxallDFMinnesota United FCMajor League Soccer −0.71z3,1561631
6Joe BellMFVikingEliteserien −0.13z2,9023321
7Matthew GarbettMFPeterborough UnitedLeague One +2.21z2,2673375
8Marko StamenićMFSwansea CityChampionship +2.21z2,6443393
9Chris Wood (captain)FWNottingham ForestPremier League +2.21z1,14359045
10Sarpreet SinghMFWellington PhoenixA-League −2.17z740283
11Elijah JustMFMotherwellPremiership −0.28z3,0887449
12Alex PaulsenGKLechia GdańskEkstraklasa −0.29z2,160080
13Liberato CacaceDFWrexhamChampionship +2.21z7871371
14Alex RuferMFWellington PhoenixA-League −2.17z2,1342260
15Nando PijnakerDFAuckland FCA-League −2.17z9680250
16Finn SurmanDFPortland TimbersMajor League Soccer −0.71z3,2440192
17Kosta BarbarousesFWWestern Sydney WanderersA-League −2.17z1,34047610
18Ben WaineFWPort ValeLeague One +2.21z1,7797319
19Ben OldMFSaint-Étienneno club data242
20Callum McCowattMFSilkeborgSuperliga −0.53z2,85212324
21Jesse RandallFWAuckland FCA-League −2.17z2,4759112
22Michael WoudGKAuckland FCA-League −2.17z2,481060
23Ryan ThomasMFPEC ZwolleEredivisie +0.74z2,1681253
24Callan ElliotDFAuckland FCA-League −2.17z1,7620110
25Lachlan BaylissMFNewcastle JetsA-League −2.17z1,850540
26Tommy SmithDFBraintree TownFA Cup +2.21z220562

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

60,395

11.0 per 1,000 of home population

Host-language familiarity

Shared

primary language English · spoken in a host

Climate adaptation gap

−5.6°C

home-vs-venue heat differential

Venue extremes

29°C

peak heat index · altitude up to 45 m

Travel

20h

max time-zone shift · nearest venue 10,799 km

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

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

New Zealand — Elo since 1951

1734 world #62
New Zealand Qualified-field median

New Zealand ends the series at 1734 Elo, the world’s 62nd-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.

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 New Zealand, 2 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →