⚽ EPL-Pulse_v1

English Premier League Match Outcome & Goals Predictor

EPL-Pulse_v1 is a leakage-safe football match prediction model trained on historical English Premier League data (1993/94 → 2024/25 mid-season).

The model estimates:

• Expected goals (xG) for home and away teams
• Outcome probabilities:
  • Home Win
  • Draw
  • Away Win
• Scoreline probability distribution (e.g., 1–0, 2–1, 0–0)

This repository contains the production-ready model artifacts used by the public Hugging Face Space.

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What’s inside this repository

Model artifacts

• home_goals_model.pkl
  Poisson regression model for home team goals
• away_goals_model.pkl
  Poisson regression model for away team goals
• feature_list.pkl
  Ordered list of features used during training
• team_state.pkl
  Latest per-team snapshot used for inference:
  • Elo rating
  • Rolling goals-for / goals-against
  • Timestamp of last update

team_state.pkl enables fast production inference without recomputing rolling features at request time.

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Modeling approach

Model type

• Poisson Generalized Linear Models (GLM)
  (one model for home goals, one for away goals)

Why Poisson?

• Goals are discrete counts
• Well-established baseline in football analytics
• Interpretable and deployable
• Produces full scoreline probability distributions

Outcome probabilities

Win / Draw / Loss probabilities are derived from the joint scoreline distribution:

P(H=i, A=j) = \text{Poisson}(i|\lambda_H) \times \text{Poisson}(j|\lambda_A)

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Features used (leakage-safe)

All features are computed strictly from matches played before kickoff.

This design prevents data leakage and supports reliable backtesting.

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Quickstart (Python)

Install dependencies

from huggingface_hub import hf_hub_download
import joblib

REPO_ID = "YOUR_USERNAME/EPL-Pulse_v1"

home_path = hf_hub_download(REPO_ID, "home_goals_model.pkl")
away_path = hf_hub_download(REPO_ID, "away_goals_model.pkl")
feat_path = hf_hub_download(REPO_ID, "feature_list.pkl")
state_path = hf_hub_download(REPO_ID, "team_state.pkl")

home_model = joblib.load(home_path)
away_model = joblib.load(away_path)
feature_list = joblib.load(feat_path)
team_state = joblib.load(state_path)