As I was looking at my PitchLogic movement visualizations that I’m using in coaching youth pitchers, I realized that I could use the quartile values to display how consistent their movement has been. The code is not that complex, so I wanted to make sure to share it. I imagine it will have considerably more uses than just my hobbyist one.
Defining the function
As my Python code for pitching assessment gets more complex, I decided to start breaking out pieces as functions. I had been using cols to create multiple charts of the various pitch types, but having a chart for each pitch type makes more meaningful charts.
For clarity, I made sure to add a good docstring…
def movementCharting (player_df, type):
""" create a chart of horizontal and vertical movement with a box around 'quantile area' showing where pitches go (separate function for creating a table of all pitch types and movement)
Args:
player_df (DataFrame): PitchLogic DataFrame with 'Horizontal Movement (in)' and 'Vertical Movement (in)'
type (string): pitch type to chart
Return:
filename (String): filename of PDF in which the image is stored
"""
Building the Chart
It only takes a few lines to build the chart using Seaborn. We’ve gotten the DataFrame that contains only pitches by one player and we trim it to only pitches of the type we’re charting.
movement_df = player_df[player_df['Type']==type]
movefig = sns.relplot(x='Horizontal Movement (in)', y='Vertical Movement (in)', data=movement_df, kind='scatter')
Then we compute our quartile values and draw the box on the plot. I’ve commented out the lines for each mean value, since they added visual complexity without making the chart more meaningful. YMMV
# Calculate mean and confidence intervals
mean_horiz = movement_df['Horizontal Movement (in)'].mean()
mean_vert = movement_df['Vertical Movement (in)'].mean()
ci_horiz = movement_df['Horizontal Movement (in)'].quantile([0.25, 0.75])
ci_vert = movement_df['Vertical Movement (in)'].quantile([0.25, 0.75])
for ax in movefig.axes.flat:
# ax.axhline(mean_vert, color='red', linestyle='--')
# ax.axvline(mean_horiz, color='blue', linestyle='--')
ax.hlines(ci_vert[0.25], ci_horiz[0.25], ci_horiz[0.75], color='blue')
ax.hlines(ci_vert[0.75], ci_horiz[0.25], ci_horiz[0.75], color='blue')
ax.vlines(ci_horiz[0.25], ci_vert[0.25], ci_vert[0.75], color='red')
ax.vlines(ci_horiz[0.75], ci_vert[0.25], ci_vert[0.75], color='red')
A couple of lines to put in the title in an appropriate spot….
movefig.fig.subplots_adjust(top=0.85) # Adjust the top to make space for the title
movefig.fig.suptitle(type + ' Movement Profile for ' + playerDisplay, y=0.90) # Move the title upward
Returning from our function
As noted in the docstring, we’re returning the filename as the value from the function. That gets dropped into an array so that it can be processed using Automate PDF Creation from Data Visualizations with Python
filename = playerfigStorage + '/' + type + ' Movement Profile.jpg'
movefig.savefig(filename)
return filename
Conclusion
Visualizations aren’t hard to create using Python and there are many ways to make them more meaningful without excessive coding. As I work with my pitchers and they learn more about using this inter-quartile range box, they might pick up some technical knowledge and understanding of how to use visualization in their own lives.
Lost in Code 