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
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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
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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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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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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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