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
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data science
A Text Analysis of Hall of Fame Speeches
Every summer, Major League Baseball inducts a small numbers of players into the Hall of Fame, with each inductee giving a speech. This
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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
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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
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Finding Similarities Among Pitchers
I’ve had several conversations recently about the nature of baseball stats compared to statistics in other sports, like football and basketball. Baseball, to
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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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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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