Understanding Common Sabermetric Statistics
WAR, wRC, SPD…all are common sabermetric stats. What do they all mean? How much insight do such stats provide over and above standard
[...]
data viz
R Analysis of Rookie Data
The below is the HTML output of an R Markdown file I recently completed for a class project. The project isn’t perfect (I
[...]
Machine Learning to Predict Player Decline
This past off season was interesting for the MLB. Mike Moustakes never signed a big contract. All-star players like Eric Hosmer and Jake
[...]
OPS Projections with Machine Learning
As spring training approaches, the excitement for a new season becomes fresh. Fans and commentators alike discuss teams’ prospects, and multiple organizations release
[...]
When Will a Pitcher Surrender a Run?
Here’s the scenario: Eighth inning in a tight game, a lefty walks to the plate against a right-handed pitcher. Any baseball fan knows
[...]
How Good (or Bad) Could the Royals Be?
I’ve always found simulation modeling to be highly informative. Given certain conditions and logic, what is likely to happen given repeated trials? What
[...]
Testing Hypotheses using Data Viz
As documented previously on this blog, I am a pretty big Royals fan. The month of July was, then, pretty exciting for me
[...]
Predicting the Next Pitch: A Markov Chain Approach
I’ve written in the past about using machine learning to predict a pitcher’s next pitch, which is a particularly difficult problem. This blog
[...]
Survival Analysis: How Long Do Careers Last?
Survival analysis inspects, well, how long an entity survives. In less morbid terms, it’s a methodology to understand time until event. This blog
[...]
Factor Analysis on HoF Stats
Factor analysis is a methodology to reduce data complexity. Factors are latent variables that represent underlying constructs in data. An interesting baseball-related application
[...]