NFL Roster DNA

Overview

The NFL Roster DNA project is a Python based scouting application designed to identify NFL organizations that best match a player's traits and scheme fit.

The project began with extensive research into all 32 NFL teams, including front office philosophies, coaching tendencies, offensive and defensive schemes, and position specific roster building preferences. That information was structured into a custom dataset and used to power an interactive application that helps evaluate potential team fits.

Users can select a position, player traits, and scheme preferences to generate a list of organizations that align with a player's profile.

Project Motivation

NFL teams often value different traits, body types, and skill sets at the same position depending on their coaching staff, schemes, and organizational philosophy.

The goal of this project was to create a centralized tool that translates qualitative scouting research into a structured, searchable system that can assist in identifying potential team fits for players.

This project combines sports research, data organization, and software development into a practical scouting application.

Dataset Development

A significant portion of this project involved building the underlying dataset from scratch.

Research was conducted on all 32 NFL organizations, including:

• General Manager roster building tendencies
• Head Coach philosophies
• Offensive Coordinator schemes
• Defensive Coordinator schemes
• Position specific player archetypes
• Preferred player traits by position
• Organizational roster construction patterns

The research was organized into structured spreadsheets and transformed into a format that could be queried programmatically.

Features

• Interactive desktop application built with Tkinter
• Position specific trait filtering
• Offensive and defensive scheme matching
• Team fit recommendations
• Dynamic dropdown menus and selection options
• Custom NFL roster DNA dataset covering all 32 teams
• Technologies Used
• Python
• Pandas
• Tkinter
• Excel
• CSV Data Processing

How It Works

• Select a player position.
• Choose relevant player traits.
• Select one or more offensive or defensive schemes.
• Run the search.
• The application returns NFL teams whose roster-building profile aligns with the selected criteria.

The recommendations are based on the custom roster DNA dataset developed for this project.

Example Use Cases

Quarterback Evaluation

A quarterback with traits such as:

• Processing ability
• Accuracy
• Pocket presence

can be matched against organizations whose offensive systems prioritize those characteristics.

Defensive Player Evaluation

A linebacker or defensive back can be evaluated against teams whose defensive schemes emphasize their strengths and skill set.

Future Improvements

Potential future enhancements include:

• Weighted team fit scoring system
• Team fit percentages
• Position specific ranking models
• Expanded scouting inputs
• Player comparison functionality
• Web based application deployment
• Automated dataset updates
• Screenshots
• Main Application Interface

Author

Ethan Friedman

• University of Wisconsin–Madison
• B.S. Data Science
• B.S. Information Science
• Computer Science Certificate

Interested in applying data, analytics, and software development to sports decision making and football operations.