Baseball Data Science – Exploring America's Pastime through Algorithms, Visualizations, and Game Theory

Predicting Winning Percentages

Earlier this season, my beloved Royals got off to a terrible start. Through 40 games, they were 14-26. The organization said they would [...]

Earlier this spring, I wanted to pull down spring training rosters and found that Wikipedia was the friendliest source. Below is the approach [...]

Who will win the World Series? That is the classic question asked before the start of every MLB season. As with many baseball [...]

This post serves as a tutorial for building machine learning models in Python. The entire codebase for this tutorial can be found on [...]

I have long hypothesized that current MLB stars are more ephemeral compared to past decades. That is, they more quickly come and go, [...]

In recent years, Major League Baseball has experienced a boom in home runs. Anyone who remotely follows the game likely know this fact. [...]

WAR, wRC, SPD…all are common sabermetric stats. What do they all mean? How much insight do such stats provide over and above standard [...]

The below is the HTML output of an R Markdown file I recently completed for a class project. The project isn’t perfect (I [...]

The shift is a major tactical tool in baseball, one that has gained substantial attention recently due to its increased use. Some teams [...]

Many people who follow the MLB know attendance is down in 2018. The season will likely end with a year-over-year attendance decline of [...]