pitchers – Analytics In The Outfield

(R)Shiny AAA Pitch Data

I first made an RShiny app in graduate school when I first discovered it in a course I was taking. Simple, yet powerful applications. Of course, I used baseball data for fun outside of my assignments. The goal was to look at pitch data and break it down by location. I wanted to bring thatContinue reading “(R)Shiny AAA Pitch Data”

Why are Angels pitchers struggling with two strikes?

Introduction Generally having two strikes is favorable for the pitcher. Hitters are hitting 0.171 in 2023 with two strikes but the Angels are giving up a BA of 0.187. For an 0-2 count it is tougher with hitters hitting 0.151 but the Angels are giving up a league worst 0.209 BA. The question is simple:Continue reading “Why are Angels pitchers struggling with two strikes?”

Clustering Starting Pitchers

My favorite machine learning algorithms are unsupervised clustering. I think there is elegance in finding patterns in data that we don’t know about. Clustering is grouping similar data together in a data set when the groupings are unknown. This is based on how far away data points are from other groups and how close theyContinue reading “Clustering Starting Pitchers”