A Review of Baseball Data Sources
Hello, readers! Its been a while. I’ve taken a slight hiatus from blogging, but I have a few projects in the pipeline. To
[...]
baseball data
Using Machine Learning to Predict Baseball Hall of Famers
Being inducted into Major League Baseball’s Hall of Fame (HoF) is the highest honor a baseball player can receive. The achievement is rare;
[...]
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
[...]
What’s Wrong with the Royals’ Offense?
As my friends know, I am a die-hard Kansas City Royals fan. When being realistic, I had modest hopes for the Royals this
[...]
Which Teams are Most Similar Offensively?
This is a short post about finding similarities among the offenses of MLB teams. To accomplish this aim, I used two statistical methodologies
[...]
Exploring 2016 Pitching Data
One of my favorite parts of baseball data is its depth. So many interesting nuances exist – we just have to look! That
[...]
Exploring 20 Years of Baseball Data (and Predicting Attendance)
Welcome back, readers! Are you ready for some more baseball data science fun? This blog has two main components: 1) exploring aggregated data
[...]
Anomaly Detection on Offensive Statistics
In early 2015, Twitter released an open-source R package for anomaly detection. Thinking this package would be a springboard for an interesting blog,
[...]
Analyzing Jake Arrieta’s No-Hitter
No question about it, the Cubs have had an impressive season. One of the reasons for their success, of course, is strong starting
[...]