🏈 Football Game Outcome Predictor – Logistic Regression (Rust)1

Status

WIP – Proof of Concept

This project is an experimental logistic regression model built in Rust using the linfa crate.
It predicts the outcome of a football game (win/loss) based on team and opponent strength metrics.

The current implementation:

• Uses a small synthetic dataset
• Trains a logistic regression model
• Evaluates accuracy using a confusion matrix
• Predicts the outcome for new input values

Planned Features

• Load real-world datasets from CSV or API
• Add more predictive features (weather, injuries, historical performance, etc.)
• Implement data normalization and preprocessing
• Support multi-class predictions (Win / Loss / Draw)
• Export trained models for reuse
• Integrate with a simple CLI or web API for predictions
• Optimize performance for high-volume predictions

Tech Stack

• Language: Rust
• ML Framework: Linfa (linfa, linfa-logistic)
• Data Handling: ndarray
• Build Tool: Cargo

Why Rust?

Rust provides:

• Memory safety without garbage collection
• C/C++-level performance
• Strong concurrency guarantees
• A growing ecosystem for numerical and ML workloads

Disclaimer

This is a proof of concept.
The dataset is minimal and purely for demonstrating the algorithm structure in Rust.

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