Project Overview
Using R as a tool and Trackman CSVs as my data, I aimed to make a report that analyzed hitters through comprehensive statistical distributions and visualizations, going beyond simple averages to reveal true hitting patterns.
Project Description
For one of my first baseball coding projects, I aimed to investigate hitter performance. When coding my hitter evaluation report, I wanted to use R to read a Trackman CSV and display statistics and plots indicating a hitter's success. In the report's creation, I cared about looking at exit velocity, launch angle, contact, hard hits, whiff, in-zone whiff, and chase.
Why Distribution Analysis Matters
Key Analytical Components
Exit Velocity Distribution
Complete distribution analysis including quartiles, revealing consistency and power across all contact events rather than just average exit velocity.
Launch Angle Distribution
Full distribution showing the true nature of batted ball profile, identifying ground ball, line drive, and fly ball tendencies that averages can obscure.
EV vs. Launch Angle Plot
Scatter plot visualization showing the relationship between exit velocity and launch angle for each batted ball.
Contact Quality Metrics
Analysis of hard hit rate, contact percentage, and overall quality of contact to evaluate offensive production capability.
Swing Decision Metrics
Evaluation of in-zone whiff rates and chase rates to assess plate discipline and pitch recognition abilities.
Example Output: Arizona Wildcats 2024
The following output is for the Arizona Wildcats' 2024 season, demonstrating how the report provides comprehensive hitter evaluations through statistical distributions and visualizations. My code works with any Trackman CSV, making it adaptable for any team or league.