MLB Statcast Hitting Leaders

Overview

This project analyzes MLB Statcast hitting leaders from the 2025 season.
Our goal is to understand different aspects of home run power—a player’s ability to hit the ball hard and far—using Statcast performance metrics.

We built a Python analysis package and an interactive Streamlit app that allow users to explore relationships between hitting variables such as home run distance, exit velocity, and barrel rate.

Data Description

We use a cleaned dataset saved as: data/combined_leaders_2025.csv

Each row represents a hitter. Important variables include:

  • hr_count – number of home runs hit

  • avg_hr_distance – average distance (in feet) of a player’s home runs

  • max_hr_distance – longest home run the player hit

  • avg_launch_speed – average exit velocity (speed of the ball off the bat)

  • max_launch_speed – maximum exit velocity recorded

  • barrels – count of “barrels,” (batted balls hit both hard and at ideal launch angles)

  • brl_percent – barrel percentage (barrels divided by all batted balls)

These metrics capture different components of hitting performance and power.

Package & Analysis Functions

The analysis.py module includes functions for:

  • loading and preparing the dataset
  • summarizing longest vs average home run distance
  • calculating correlations between metrics
  • ranking players by barrel rate
  • examining workload vs performance
  • detecting outliers
  • producing scatterplots for all major relationships

Examples of how to use these functions appear on the Tutorial page.

Streamlit App

We built a Streamlit dashboard to visualize hitter performance interactively.
Users can:

  • filter hitters by minimum home runs
  • explore longest HR distances
  • examine correlations
  • visualize relationships between power metrics
  • view statistical standouts

Run locally with:

streamlit run streamlit.py

or visit our hosted streamlit app.