A lot of being a great pitcher is related to being different or not average, like having a very low or high spin rate on a fastball. That trend is obvious with a certain pitcher; Tyler Rogers of the San Francisco Giants. As many of you know, Rogers is a submarine pitcher and has an... Continue Reading →
Making Pitching Decisions Based on Predictive Modeling Part 2: In-Game Usage
In my last article, I built a model to predict the performance of every single at-bat to help with making bullpen decisions. As a reminder, I used a random-effects model with the random variables being times through the order and platoon matchup and the fixed effects being pitcher and hitter wOBAs. I decided I wanted... Continue Reading →
Making Pitching Decisions Based on Predictive Modeling
Baseball is all about matchups. It is called the most individual team sport for a reason, as pitchers and hitters are all alone to compete against one another. Those matchups create scoring, either by the batter winning multiple in a row, or getting the most out of it and putting a ball into the seats.... Continue Reading →