Architectural Deep-DiveML Developer

fpredict

A data-driven predictive sports analytics engine compiled for English Premier League match cycles. Employs historical sequence datasets, expected goals calculations, and feature engineering to model upcoming fixture outcomes.

PythonScikit-LearnTensorFlowHTML

Source Code

System Challenge

Building a predictive engine capable of digesting highly noisy, multi-variate historical football datasets (xG, sequence lengths, fatigue) into accurate match outcome probabilities.

Architecture & Solution

Designed an expansive feature-engineering pipeline using Python and Scikit-Learn to normalize time-series statistics. Built and trained a multi-layer perception (MLP) using TensorFlow, wrapped in a lightweight HTML interface for visualization.

Performance Outcomes

Achieved statistically significant predictive accuracy margins over baseline bookmaker models.
Automated the continuous ingestion and data-cleansing pipeline for weekly Premier League stats.
Developed a scalable architecture ready to absorb additional league datasets.