Enhance Football Prediction System with ML

We are looking for an experienced Machine Learning / Football Prediction / Sports Analytics expert to improve our existing football 1X2 prediction system.

IMPORTANT: The system is ONLY for these 5 leagues:

1. Premier League
2. La Liga
3. Serie A
4. Bundesliga
5. Ligue 1

No other leagues are required.

PROJECT REQUIREMENT:

We already have a production-grade football prediction pipeline built specifically for these 5 leagues.

The system currently includes:
- V1 / V2 / V3 / V4 prediction models
- Poisson goal modelling
- Venue/home advantage modelling
- Pre-match Elo ratings
- Online attack/defence features
- Dixon-Coles draw modelling
- Logistic stacking
- Draw-calibrated research candidate
- Strict chronological/out-of-sample validation
- Production monitoring and prospective validation pipeline

Current production model:
v4_draw_champion
v4.0-champion-dc-elo-stacking

We have already evaluated the system extensively on historical 2025/26 data from the 5 leagues.

Current diagnostic performance is around 52% top-1 1X2 accuracy.

Our goal is to significantly improve genuine prediction performance, with 85% accuracy being an aspirational long-term target — NOT a guaranteed requirement.

WHAT WE NEED FROM YOU:

- Independently audit our complete existing prediction architecture.
- Understand why the current model is around 52% accuracy.
- Investigate Home / Draw / Away prediction performance separately.
- Particularly investigate Draw prediction and probability calibration.
- Identify weaknesses in our current features, modelling, calibration and decision layer.
- Explore better ML models, ensembles, feature engineering, probability calibration and statistically valid approaches.
- Improve prediction performance specifically for the 5 target leagues.
- Preserve strict chronological / pre-match causality.
- Absolutely no future-data leakage.
- No post-match information may enter prediction features.
- Compare every proposed approach against our existing V4 and Draw Champion baselines.
- Provide reproducible code and measurable results.

REQUIRED EVALUATION:

For every candidate approach, report:

- Accuracy
- Home / Draw / Away accuracy
- Confusion matrix
- Draw precision / recall / F1
- Log Loss
- Brier Score
- Ranked Probability Score (RPS)
- Probability calibration / ECE
- Per-league performance
- Chronological performance
- Betting / expected-value performance if applicable

IMPORTANT:

We do NOT want an artificial 85% result created by overfitting historical data.

We want genuine out-of-sample improvement.

The existing production models and frozen validation datasets must remain protected unless changes are explicitly approved.

The freelancer should be comfortable working with:
- Python
- Machine Learning
- Football analytics
- Poisson models
- Dixon-Coles
- Elo ratings
- Ensemble / stacking
- Probability calibration
- Time-series / chronological validation
- Sports prediction
- Data leakage prevention

TIMELINE:

We are initially looking for an intensive 2-day research/engineering sprint.

DELIVERABLES:

1. Full technical audit
2. Identified weaknesses
3. Experimental approaches
4. Best-performing candidate model
5. Reproducible implementation
6. Full validation results
7. Per-league analysis for all 5 leagues
8. Clear explanation of what improved and why
9. Honest assessment of achievable accuracy
10. Recommended next steps for production deployment

Please apply only if you have real experience with sports prediction, football analytics, machine learning forecasting, or similar quantitative prediction systems.

Please include relevant previous projects/results in your proposal.