Applying Data Science and Machine Learning to Baseball
In the second part of this four part series, I will look at how well hitter walk rate can be modeled by plate discipline. I highly recommend reading the first installment, as much of the methodology is the same. Due to this similarity, I will omit most of the methodology in this piece. Methodology I …
Continue reading “Modeling Walk Rate with Plate Discipline Part 1: Hitters”
Strikeout and walk rates are perhaps the most popular and widely used peripheral statistics, particularly for pitchers. However, with pitch level data, these statistics now have “peripherals” of their own. I was curious if I could create an accurate yet interpretable model using FanGraphs’ plate discipline metrics that could offer insight on what drives the …
Continue reading “Modeling Strikeout Rate with Plate Discipline Part 1: Hitters”
With the advent of Statcast, statistics like exit velocity, spin rate, and launch angle have become easily accessible to baseball fans. Catcher pop time data, too, has become available from Statcast. However, unlike some of the other Statcast metrics, catcher pop time data has existed for much longer, with scouts measuring pop times in the …
Continue reading “Reframing Catcher Pop Time Grades using Statcast Data”
Introduction As an LSU alumni, Aaron Nola has long been said to be at his best in blistering hot weather. Many writers and Phillies fans believe that warm, muggy weather meaningfully improves his pitching performance. I wanted to see if I could find any evidence for this belief. Here is a graph looking at Nola’s …
Continue reading “Does Aaron Nola pitch better in hot and humid weather?”
Introduction Last week, I posted an article on the best pitch type and location combinations in the strike zone. In that analysis, I included swinging and called strikes and assigned these pitches an xwOBA of zero. I noted that one could argue this choice gives too much weight to strikes relative to batted balls. Today, …
Introduction A common thread in sabermetric research is looking at underlying metrics rather than purely results (Ex: strikeout rate, exit velocity, O-swing% rather than simply OPS). Recently, I’ve been thinking about going even further with this concept than strikeout/walk rates with pitchers and instead examining pitch-by-pitch data for pitcher evaluation. While this article is not …
Continue reading “What pitch type and pitch location combos are most effective in the strike zone?”
Hello and welcome to my blog, Saberscience! My name is Ishaan and I am a data science undergraduate student at UTD. I started this blog as a means of sharing my baseball thoughts through a statistical lens. In my analyses, I primarily intend to use the programming language R and , eventually, machine learning, which …
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