Project Overview
Using R as a tool and Trackman CSVs as my data, I was able to craft a report that analyzes pitch sequencing by investigating how different pitch types perform following specific pitches, revealing strategic patterns and optimal sequencing strategies.
Project Description
When making my pitch sequencing report, my goal was to investigate how pitchers' different pitch types performed following a specific pitch type. This analysis reveals which pitch combinations are most effective and helps identify optimal sequencing patterns for each pitcher's arsenal.
Metrics Analyzed for Each Sequence
Analytical Methodology
NCAA-Benchmarked Color Coding System
For better interpretability, statistics where I felt it would be useful for them to be compared to league averages are automatically color coded on a red to green scale where green is good for the pitcher. The coding is based on the rolling NCAA average and standard deviation for each statistic.
The color scales are different for each pitch type because, for example, league average whiff percentage for a fastball is lower than league average whiff percentage for a slider. Therefore, each pitch type is color coded based on its custom scale, providing pitch-type-specific context for performance evaluation.
Strategic Applications
This report helps pitchers and coaches understand which pitch combinations generate the best results. For example, it can reveal that a slider is particularly effective after a fastball, or that a changeup following a curveball generates more swings and misses than following a fastball. These insights inform in-game pitch calling and help develop optimal sequencing strategies tailored to each pitcher's strengths.
Example Output: Iowa 2025 Season
This report includes all of the data from the 2025 Iowa baseball season, demonstrating comprehensive team-wide sequencing analysis. The code can be used to analyze any Trackman data, making it adaptable for any team or league.