The First Pitch Effect

Project Overview

In the modern era of baseball analytics, few metrics are preached as loudly as the First Pitch Effect (FPE). The philosophy is simple: get ahead 0-1, and the batter is on the defensive. Fall behind 1-0, and the pitcher is at the mercy of the hitter.

But does the data back up the dogma?

This project analyzes Major League Baseball (2024 Season) pitch-by-pitch data to determine exactly how the outcome of the very first pitch dictates the final result of the at-bat.

The Core Question

We are not just looking at batting averages; we are looking at the anatomy of the outcome.

  • When a batter gets a Hit, did it usually start with a Ball or a Strike?

  • When a batter walks, was the first pitch almost always a ball?

  • How many Home Runs are surrendered after falling behind early?

Methodology

Using R and the baseballr package, I scraped and processed raw Statcast data for the entire 2024 MLB Regular Season. This dataset tracks every single pitch thrown this year to isolate the first pitch of every at-bat and link it to the final conclusion.

  • Data Source: MLB Statcast (Full 2024 Season)

  • Scope: 2,430 Games / ~180,000 At-Bats

  • Tools: R, Tidyverse, Quarto

  • Visualization: Compositional Analysis (Pie Charts)

How to Navigate

Use the Analysis dropdown menu at the top to explore specific outcomes:

  • FPE vs Hits: Do hits come primarily from 1-0 counts?

  • FPE vs Home Runs: The cost of the first-pitch mistake.

  • FPE vs Outs: How often does a First Pitch Strike lead to an easy out?

  • FPE vs Walks: How often does a first-pitch strike prevent a walk?

  • FPE vs Strikeouts: The correlation between 0-1 starts and the K.