WC2026 Match Predictor — ml-xgboost-v1
A 3-class (HOME_WIN / DRAW / AWAY_WIN) gradient-boosted classifier predicting FIFA
World Cup 2026 match outcomes from team-strength features.
- Algorithm: scikit-learn
HistGradientBoostingClassifier(gradient-boosted trees) - Training data: sergiopesch/wc2026-internationals — 499 historical internationals between the 48 finalists
- 5-fold CV accuracy: 0.545 (vs 0.455 majority-class baseline)
- Live demo + API: sergiopesch/wc2026-match-predictor Space
Features
Engineered to be symmetric / rating-based so they generalise monotonically (raw rank/points levels were deliberately excluded — they let the trees memorise this friendly-heavy sample and invert on unseen mismatches):
| feature | meaning |
|---|---|
rank_diff = away_rank − home_rank |
>0 favours home |
pts_diff = home_pts − away_pts |
>0 favours home |
home_is_host |
home side is a 2026 host (USA/Mexico/Canada) |
Usage
import joblib, pandas as pd
from huggingface_hub import hf_hub_download
model = joblib.load(hf_hub_download("sergiopesch/wc2026-match-predictor", "model.joblib"))
X = pd.DataFrame([{"rank_diff": 11 - 5, "pts_diff": 1776 - 1694, "home_is_host": 0}])
print(dict(zip(model.classes_, model.predict_proba(X)[0])))
Or call the hosted API:
curl -X POST https://sergiopesch-wc2026-match-predictor.hf.space/predict \
-H "Content-Type: application/json" \
-d '{"home_rank":5,"home_pts":1776,"away_rank":11,"away_pts":1694,"home_is_host":0}'
How it's used
Registered in Salesforce Einstein Studio as a Bring-Your-Own-Model, scoring live 2026 fixtures from a Data Cloud feature pipeline — one of four prediction engines in a World Cup demo (alongside a transparent rules model, an Elo rating system, and a native Einstein Studio model).
Limitations
Three coarse features capture broad strength gaps, not form, injuries, or tactics. Educational/demo use; predictions are illustrative. Licence: CC0-1.0.
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