nfl-offensive-playcalling-optimization's Introduction

Hello there! 👋

I'm Jacob, and I like to work with data. I build various projects in the AI products and analytics spaces -- both professionally and in my spare time.

My Day Job

I currently work at Avodah as the head of engineering for the AvodahConnect business unit. I have the pleasure of leading a global engineering team in building AI products that provides solutions for real-time translation, transcription, language preservation, and language education.

What I'm Learning

In the evenings, I'm trying to continuously improve my full-stack engineering skillset to better assist the full-stack teams teams I lead. (Academically and professionally I've spent more time in machine learning than application development.) I'm currently tinkering with small scale applications on Next.js.

What I'm Working On

Outside work and studies, here's what you'll find me doing:

Tinkering with the latest fad in the machine learning community (right now, it's the open source LLMs and RAG-based patterns)
Creating toy models for sports analytics
Working on small contributions to OSS libraries

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nfl-offensive-playcalling-optimization's Issues

Implement Temporal Difference Learning Agent

Q-Learning optimizes policy on just passing every play

When implementing Q-Learning on the field-position agnostic environment, the learned policy converges on the idea of passing every single play.

Implement DQN Agent

Implement Punts, Field Goals and Touchdowns in DataLoader class

Possible for yardage to be greater than field position

Using the yardline_100 column in the loaded dataset, it's possible for a play to eclipse the presumed field position on the field. For example yardline_100 = 20 is assumed to be on the 20 yard line, but it's possible to get plays where the yards_net is greater than 20 yards.

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