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
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Predicting Tomorrow’s Outcome
Predicting the outcome of future baseball games is notoriously difficult. Some common methodologies are slightly better than a coin flip. If you always
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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
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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
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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
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An In-Depth Analysis of Clayton Kershaw
Clayton Kershaw has been one of the most dominant pitchers in the MLB over the past several years. At only 28, he’s already
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Association Rules Mining on 2016 Game Logs
Since my beloved Kansas City Royals did not make the playoffs, I almost cannot bring myself to pen a post about October baseball.
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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
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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,
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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
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