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Mountain Range

Portfolio

Check back often for new projects and links to Github! Some new, some old! Some good, some not so good. Use them as inspiration, borrow some code, or just check them out!

2022 SABR Analytics Conference Presentation

Click through to watch my presentation on Exit Velocity Over Expected (EVOE) at the 2022 SABR Analytics Conference.

NFL Big Data Bowl 2022

Click through to the GitHub Repository associated with the project.

Exit Velocity Over Expected (EVOE)

Click the button to access two notebooks that show how I conducted the EVOE project.

NFL EPA Calculator

Click this link to go to the GitHub repository for my NFL EPA Calculator API. Click the main link to take you to the API. 

How to Leverage a Portfolio to Break Into Data Science/Analytics

I was asked to speak during a webinar on the topic above. Follow the link for some tips and tricks to consider when creating your own portfolio!

MLB Next Pitch Predictor - API

This is a follow-up of the Next Pitch Predictor. I created an API with Streamlit and Heroku to allow for a user to predict the next pitch for a given at-bat. 

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GitHub Link

MLB Next Pitch Predictor

For my Spring 2020 Neural Networks and Deep Learning class, I used an MLP NN to predict the next pitch type of an at-bat (dependent on the pitcher),

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Follow the link to the Github code. 

MLB - Am I Underpaid?

I used kMeans Clustering to explore salary inequalities based on wOBA metrics for MLB batters during the 2016 season. 

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Follow the link to the Github code. 

Machine Learning - Baseball Data

I used various Machine Learning algorithms to develop models to predict numerical batting stats (wOBA, Runs, Hits, etc) and categorical awards (All-Star and MVP) of Major League Baseball Players between 2000-2019.

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Follow the link to the Github code. 

Season Runs Simulator

Debate on Twitter sparked an interest in exploring how to construct a lineup that would score the most runs. To be specific, this was on White Sox Twitter in February 2020 and I was inspired to create a runs simulator that takes a lineup as the input and spits out the total number of runs produced in a season!

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Check out my previous blog post about this project and follow the link to the Github code. 

NBA Statistical Analysis

In May 2019, I completed a project that used Linear Regression, PCA, Factor Analaysis, and Canonical Correlation Analysis to evaluate NBA data. 

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Follow the link to the powerpoint. 

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