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
[...]
sabermetrics
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
[...]
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
[...]
Can Latent Class Analysis Identify Chris Sale’s Pitches?
Latent class analysis (LCA) is one of my favorite analytical techniques. Essentially, as the name indicates, the algorithm attempts to discover latent classes
[...]
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,
[...]
Clustering (Almost) Every MLB Team…Ever
Welcome to blog number three! This piece seeks to cluster MLB teams based on their offensive and defensive stats. I used the teams
[...]
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
[...]
Inaugural Blog: Eric Hosmer’s Statcast Data
Welcome to baseballdatascience.com! This site combines two of my passions: baseball and data science. I look forward to (hopefully) bringing you fresh, quantitative-based viewpoints
[...]