BettorsOracle is a machine learning–driven web application for predicting football match outcomes using statistical modeling and historical performance data.
The project aims to research, design, and deploy predictive systems capable of estimating match probabilities (win/draw/loss and other betting markets) through data-driven methodologies.
This repository represents an active research and development effort. The system is currently under continuous improvement and structural refinement.
Development Status: Active (Work in Progress)
This project is not yet production-stable. Core models are under experimentation and validation.
Upcoming improvements include:
- Data pipeline restructuring
- Model performance optimization
- Feature engineering expansion
- API layer development
- Deployment infrastructure design
- Build reliable predictive models for football outcomes
- Implement automated data ingestion pipelines
- Evaluate model performance using statistical validation metrics
- Create a scalable backend for model inference
- Develop a web-based interface for prediction visualization
- Continuously improve prediction accuracy through research and iteration
Data Layer
- Historical match datasets
- League performance metrics
- Team statistics
- External contextual features
Processing Layer
- Data cleaning and normalization
- Feature engineering
- Model training and evaluation
Modeling Layer
- Classification models (e.g., Logistic Regression, Random Forest, Gradient Boosting, Neural Networks)
- Ensemble methods
- Cross-validation strategies
- Performance tracking
Application Layer
- Backend API (planned)
- Model inference engine
- Web application frontend
Deployment Layer (Planned)
- Containerization (Docker)
- Cloud hosting
- CI/CD pipeline
MODEL2024/ Contains experimental and production candidate model files.
MoScOv.xlsx Structured dataset and statistical input source.
New-120betting-120model (AutoRecovered) Model iteration draft and testing artifacts.
workplan.docx Project development and strategy planning document.
Note: Repository structure will evolve as development progresses.
Prerequisites:
- Python 3.9+
- pip
- Virtual environment recommended
Example setup:
git clone https://github.com/emmanuelebube13/BettorsOracle.git
cd BettorsOracle
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txtFurther setup instructions will be documented as the application layer is finalized.
This project follows:
- Empirical validation over assumptions
- Backtesting before deployment
- Strict separation of training and evaluation datasets
- Continuous performance monitoring
- Conservative interpretation of predictive probabilities
No model is considered valid without statistical validation.
Phase 1 – Data Structuring
- Clean historical datasets
- Normalize team performance metrics
- Build reusable data ingestion pipeline
Phase 2 – Model Optimization
- Hyperparameter tuning
- Cross-league generalization testing
- Reduce overfitting
Phase 3 – Backend API
- RESTful prediction endpoints
- Authentication system
- Usage logging
Phase 4 – Frontend Application
- User interface for match selection
- Prediction visualization
- Probability breakdown
Phase 5 – Deployment
- Docker containerization
- Cloud infrastructure
- Domain and production rollout
This is currently a private research-driven project.
External contributions are not automatically accepted.
If collaboration is desired:
- Submit a detailed proposal via email or issue request
- Clearly define the technical value you intend to add
- Do not submit pull requests without prior discussion
Unauthorized forks or derivative commercial implementations may violate licensing terms.
BettorsOracle is a research and analytical project.
The predictions generated by this system are probabilistic estimates based on historical data and statistical modeling. They do not guarantee outcomes.
Users assume full responsibility for any financial decisions made based on model outputs.
The authors are not liable for financial losses or betting outcomes.
All code, models, data structures, methodologies, documentation, and associated materials in this repository are the intellectual property of the project owner unless otherwise stated.
Reverse engineering, replication of modeling strategies, redistribution, or commercial use without explicit written authorization is strictly prohibited.
Proprietary License – All Rights Reserved
Copyright © 2026 Emmanuel Mbachu. All Rights Reserved.
This software and associated documentation files are proprietary and confidential.
Permission is NOT granted to:
- Distribute
- Sublicense
- Sell
- Create derivative works
Without prior written consent from the copyright holder.
Unauthorized use, reproduction, or distribution may result in legal action.