NFL Big Data Bowl — Outcome Prediction Modeling

Overview

This project develops machine learning models to predict play outcomes using player tracking data from the NFL Big Data Bowl dataset. The workflow focuses on transforming high-resolution spatial tracking data into structured predictive features and building end-to-end modeling pipelines for competition-style evaluation.

Objectives

• Convert raw tracking and play-level data into modeling-ready datasets
• Engineer spatial and contextual features representing player movement and positioning
• Train and evaluate predictive models aligned with the competition evaluation metric
• Generate reproducible submission outputs and experiment tracking

Methodology

Data preprocessing

• Tracking data alignment and cleaning
• Play-level dataset construction
• Feature normalization and transformation

Feature engineering

• Player positioning features
• Movement-based aggregate metrics
• Contextual play information features

Modeling

• Baseline model development
• Iterative model refinement and validation
• Final model selection and submission generation

Repository Structure

data/ # raw and processed datasets
notebooks/ # modeling and experimentation notebooks
outputs/ # prediction outputs and submission files
src/ # preprocessing and modeling utilities

Results

Models were evaluated using the official competition metric, and final prediction outputs were generated for submission benchmarking and performance comparison.