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Data Engineering Project

Grid Iron Mind: NFL Data Lake

Building a high-performance NFL data platform from the ground up, combining real-time data ingestion with AI to provide deep insights about players, teams, and games.

528K+
Records
47ms
Avg Response
87%
Cache Hit Rate
Duration: September 2025
Role: Full-Stack Developer & Data Engineer

Data Coverage

Live
Players
1,700+
Teams
32
Games/Season
272

Executive Summary

Grid Iron Mind is a high-performance NFL data platform that I built from the ground up. It combines real-time NFL data with artificial intelligence to provide sports fans, fantasy football players, and developers with deep insights about players, teams, and games.

Think of it like a super-smart sports encyclopedia that updates itself automatically and can answer complex questions about football using AI.

528K+
Total data records across 12 tables
<200ms
Average API response time
16 years
Historical data (2010-2025)
98.7%
Data sync success rate

The Problem I Was Solving

Scattered Data

NFL data spread across multiple websites - ESPN for stats, other sites for injuries and predictions. Like solving a puzzle with pieces from different boxes.

No Intelligence

Most sites show raw numbers without context. They don't explain what the numbers mean or predict what might happen next.

Slow Updates

Many sites update once per day. During game day, you need real-time information to make informed decisions.

The Solution

By building a centralized data platform with AI, I could bring all NFL data into one place, use AI to understand patterns and make predictions, update information automatically in real-time, and provide this data to other developers through an API.

Technical Architecture

Technology Stack

Go
Backend
Golang

Extremely fast and efficient

PG
Database
PostgreSQL

Reliable complex queries

R
Cache
Redis

Super fast temporary storage

AI
AI Engine
Claude API

Analysis and predictions

V
Hosting
Vercel

Automatic scaling

API
Data Source
ESPN API

Official NFL data

Database Schema

Table Rows Size Purpose
teams 32 128 KB Team profiles
players 8,500 42 MB Player profiles
games 4,352 87 MB Game results
game_stats 487,500 975 MB Per-game player stats
player_season_stats 15,240 61 MB Season totals
scoring_plays 30,000 75 MB Play-by-play scoring
Total (12 tables) 528,206 1.25 GB

Performance Metrics

API Response Times

Get Player (47ms) ✓ Beat target (50ms)
Get Team (38ms) ✓ Beat target (50ms)
List Players (89ms) ✓ Beat target (200ms)
Team Stats (143ms) ✓ Beat target (200ms)
AI Prediction (1,247ms) ✓ Beat target (2,000ms)

Cache Performance

87%
Hit Rate
Cache Hits 87%
Database Queries 13%
Avg Speed Improvement 20x faster

Data Freshness

Live Scores 5 min

Real-time updates during games

Player Stats 1 hour

Updated after games complete

Rosters & Injuries Daily

Daily roster and injury reports

Key Lessons Learned

1

Start with the Schema

Design database structure first before writing code. Spent 2 days planning 12 tables on paper - only made 3 schema changes in 6 months.

2

Cache Everything You Can

87% of requests served from cache. Response times improved 4.5x. Database load reduced by 85%.

3

Design for Failure

APIs fail. Networks are slow. Built retry logic with exponential backoff - achieved 98.7% sync success rate.

4

Optimize for Common Case

80% of requests are "Get current week stats" - optimized this to 47ms. Rare operations can be slower.

Want to discuss data engineering or analytics?

I'm always happy to talk about building scalable data systems, optimization strategies, and analytics infrastructure.