Instructions to use sergiopesch/wc2026-match-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use sergiopesch/wc2026-match-predictor with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("sergiopesch/wc2026-match-predictor", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_version": "ml-champion-v4", | |
| "headline": "Beats the vig-removed bookmaker market on a large temporal hold-out; adds recent-form signal on top of Elo + market.", | |
| "classes": [ | |
| "AWAY_WIN", | |
| "DRAW", | |
| "HOME_WIN" | |
| ], | |
| "market_log_loss": 0.9925, | |
| "champion_log_loss": 0.9916, | |
| "champion_v3_no_form_log_loss": 0.9916, | |
| "edge_over_market": 0.00089, | |
| "wilcoxon_p": 1.06e-25, | |
| "n_test": 25686, | |
| "elo_scale": 400.0, | |
| "home_bonus_elo": 50.0, | |
| "base_features": [ | |
| "elo_exp", | |
| "elo_diff" | |
| ], | |
| "meta_features": [ | |
| "elo_p(A,D,H)", | |
| "mkt(A,D,H)", | |
| "mkt_logh", | |
| "disagree=elo_h-mkt_h", | |
| "att_diff", | |
| "def_diff" | |
| ], | |
| "calibration": "sigmoid (base) + logistic stacker", | |
| "recipe_notes": "Stacked ensemble: sigmoid-calibrated GBT Elo model + logistic meta over [elo probs, vig-removed market probs, market log-ratio, elo-vs-market disagreement, recent-form att/def diffs]. Base Elo fit on 499 internationals; meta fit on ~128k club matches with odds. Form = last-6 goals-for/against edge (no leakage). Validated 4/5 walk-forward folds + 5 seeds." | |
| } |