Wrangling Major League Baseball Pitchf/x Data with Python

Parse each pitch in an at bat from pitchf/x data with Python, identify the pitch type, and maintain a running total of pitches while printing a verbose, color-coded display.

Coerce nested pitch data into a pandas data frame, producing one row per pitch with batter, at-bat index, stand side, speed, and location for efficient bat-by-bat analysis.

Use data frame slices to inspect mlb pitch data, applying non-inclusive ranges and at bat or batter filters to pull targeted rows and pitches.

Chart pitches against the strike zone by plotting called strikes and balls for right- and left-handed batters, using a Python workflow with data frames, color dictionaries, and markers.

Chart Kershaw's balls and strikes by right- and left-handed batters using Python, plotting pitch locations relative to the strike zone with handedness annotations.

Filter the pitch data frame to Dickerson's first at bat and plot the six pitches against the strike zone, then iterate plots in a notebook with one line of code.

Add ball and strike count columns to the pitch data frame with Python, initialize at 0-0, and update counts per pitch for each at-bat using ball, foul, and strike events.

We pulled 257 pitches from Kershaw's no-hitter into a Jupiter notebook, wrangled from Excel to a data frame, and plotted pitch locations and tendencies.

Discover how pitchf/x data and spin rate analytics reshape baseball strategy, from pitcher adjustments to player development, using public data and video analysis to measure kinetic chain impact.