5/21/22

Hi everyone! It's a busy day for Connecting the Game as I have been working on a lot of different projects over the past few months and really want to share them.

I recently finished my first class in data analytics, an introduction to data science course going over the R programming language and several statistics concepts related to data analysis. For our final project, we had to write a report on a dataset that we were interested in and use the modeling and analytical techniques we learned to process, visualize, and interpret our data. I chose to analyze the 30 MLB franchises from 1998-2021 and try to find similarities and groups between the franchises while also using their performance to possibly predict how well an expansion team might perform in today's MLB.

I would write a whole big explanation about my report, but I think I should let it speak for itself. To check out the final report, use this link: Data Analytics Papers. I also used supplementary GitHub repo for this project which includes some explanations, outputs, and the dataset I used for this project. Here is that link: Analyzing MLB Franchises Since 1998

I really thought that the outline I used help organize the paper and make a solid foundation for how to approach a data analytics problem. This was helpful especially because we had to use both unsupervised and supervised learning (I go into more detail about them in my report) and discuss modeling and results.

I hope you enjoy!

-Andrew