WC 2026 · Forecasting Oxford Football Forecasting
🇭🇹

Haiti

CONCACAF Group C
0.0% Champion probability ±0.00 MC-SE
Coach
Sébastien Migné foreign · French
Elo (model)
1,548 world 71st
Squad value
€44M
Power → Reality
46th 47th −0.00 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Haiti — stage progression

Round of 32: 11.63% (95% MC 11.43%–11.83%; MC-SE ±0.10 pts) Round of 32 reach 11.6% ±0.10 Round of 16: 1.37% (95% MC 1.29%–1.44%; MC-SE ±0.04 pts) Round of 16 reach 1.4% ±0.04 Quarter-final: 0.18% (95% MC 0.15%–0.20%; MC-SE ±0.01 pts) Quarter-final reach 0.2% ±0.01 Semi-final: 0.02% (95% MC 0.01%–0.03%; MC-SE ±0.00 pts) Semi-final reach 0.0% ±0.00 Final: 0.00% (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

Haiti is most likely eliminated before the knockout rounds: 12% to clear the group. Champion probability 0.00%.

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 Haiti against that side. Full bracket & collision matrix →

Match 5 · 2026-06-14 · Boston Stadium home
12.7% win 23.7% draw 63.6% loss
Most likely 0–1 (14.1%) λ 0.69–1.81 Over 2.5 46% · BTTS 42%
Match 29 · 2026-06-20 · Philadelphia Stadium away
Haiti Brazil
2.1% win 7.4% draw 90.5% loss
Most likely 0–3 (13.8%) λ 0.46–3.42 Over 2.5 74% · BTTS 36%
Match 50 · 2026-06-24 · Atlanta Stadium away
Haiti Morocco
6.9% win 20.1% draw 73% loss
Most likely 0–2 (17.3%) λ 0.45–1.96 Over 2.5 43% · BTTS 32%
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 Haiti. 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

Haiti vs the field

Elo rating: 1548 vs field median 1780 (0.87× the field) Elo rating 1548 med 1780 Recent NT form: 1.27 ppg vs field median 1.87 ppg (0.68× the field) Recent NT form 1.27 ppg med 1.87 ppg Squad value: €44M vs field median €286M (0.15× the field) Squad value €44M med €286M Squad form (global): 0.115 vs field median 0.211 (0.55× the field) Squad form (global) 0.115 med 0.211 Fitness readiness: 0.398 vs field median 0.707 (0.56× the field) Fitness readiness 0.398 med 0.707 Familiarity / chemistry: 0.003 vs field median 0.015 (0.20× the field) Familiarity / chemistry 0.003 med 0.015 Experience (mean caps): 23 vs field median 25 (0.92× the field) Experience (mean caps) 23 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

Haiti on the decoupling axis

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

g = −0.17 ± 0.10: 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
24.8mean age
23mean caps
31%in a top-5 league
25distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Johny Placide (captain)GKBastiaLigue 2 +1.70z2,1280810
2Carlens ArcusDFAngersLigue 1 +1.70z2,1570531
3Keeto ThermoncyDFYoung Boysno club data10
4Ricardo AdéDFLDU QuitoLiga Pro −0.72z4,7832592
5Hannes DelcroixDFLuganoSuper League −0.07z1,155170
6Carl SaintéMFEl Paso Locomotive FCno club data260
7Derrick Etienne Jr.FWToronto FCMajor League Soccer −0.71z1,0070488
8Martin ExpérienceDFNancyLigue 2 +1.70z1,4521210
9Duckens NazonFWEsteghlalPersian Gulf Pro League −0.98z14817844
10Jean-Ricner BellegardeMFWolverhampton Wanderersno club data100
11Louicius DeedsonFWFC DallasMajor League Soccer −0.71z2203210
12Alexandre PierreGKSochauxno club data150
13Duke LacroixDFColorado Springs Switchbacks FCUSL Championship −0.71z6580163
14Leverton PierreMFVizelaSegunda Liga +1.14z170330
15Ruben ProvidenceFWAlmere CityEerste Divisie +0.74z1,1021153
16Lenny JosephFWFerencvárosNB I1,8251121
17Danley Jean JacquesMFPhiladelphia UnionMajor League Soccer −0.71z4,9053306
18Wilson IsidorFWSunderlandPremier League +2.21z1,365642
19Yassin FortunéFWVizelaSegunda Liga +1.14z414140
20Frantzdy PierrotFWÇaykur RizesporSüper Lig +0.49z12705134
21Josué CasimirFWAuxerreLigue 1 +1.70z1,532170
22Jean-Kévin DuverneDFGentJupiler Pro League −0.07z1,1741171
23Josué DuvergerGKCosmos Koblenzno club data60
24Wilguens PaugainDFZulte WaregemJupiler Pro League −0.07z1,298180
25Dominique SimonMFTatran PrešovSuper Liga576020
26Woodensky PierreMFVioletteno club data10

Source · Official squad announcements · API-Football (global club coverage). 6 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 77%.

Diaspora in the hosts

811,820

69.0 per 1,000 of home population

Host-language familiarity

Shared

primary language French · spoken in a host

Climate adaptation gap

+4.7°C

home-vs-venue heat differential

Venue extremes

37°C

peak heat index · altitude up to 313 m

Travel

1h

max time-zone shift · nearest venue 1,159 km

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

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

Haiti — Elo since 1950

1707 world #71
Haiti Qualified-field median

Haiti ends the series at 1707 Elo, the world’s 71st-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.

77% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
77% 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 Haiti, 6 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →