Undrafted Analytics
NFL Analytics Podcast
German football analysis powered by data science
1,000+
Simulations
40+
State Nodes
22+
Episodes
5
Seasons
The Problem
German NFL fans lacked data-driven analysis in their native language. Traditional sports predictions rely on simplified models that don't capture the full complexity of football games - play types, situational factors, and team-specific tendencies. There was no platform combining rigorous statistical analysis with accessible German commentary.
The Solution
Created a two-part technical platform: (1) A Monte Carlo simulation engine that predicts NFL game outcomes by simulating complete games play-by-play using a graph-based state machine with 40+ nodes modeling all play types, and (2) A JAMstack podcast website with data visualizations, full-text search, and podcast integration. The simulation uses KDE-based probabilistic sampling with defense-adjusted factors from 5 seasons of historical data.
Tech Stack
Simulation
Data
Frontend
Infrastructure
My Role: Co-founder, Co-host & Technical Lead
- Co-founded German NFL analytics podcast
- Designed and built Monte Carlo simulation engine from scratch
- Created graph-based state machine with 40+ nodes modeling NFL game flow
- Implemented KDE models for probabilistic play outcome sampling
- Built ETL pipeline for PostgreSQL historical data (5 seasons, 32 teams)
- Developed Vue.js/Gridsome podcast website with dark mode and search
- Co-hosted weekly podcast episodes with data-driven analysis
Key Differentiators
End-to-End Data Science Platform: From raw NFL data to published predictions
DOT-Based Game Definition: Entire NFL game flow in human-readable Graphviz format
Novel Defense Factor: KDE-based team vs league defensive distribution comparison
German Market Niche: First German-language NFL analytics podcast with rigorous data science
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