Update functions/pitch_summary_functions.py

functions/pitch_summary_functions.py

@@ -261,7 +261,7 @@ def velocity_kdes(df: pl.DataFrame, ax: plt.Axes, gs: gridspec.GridSpec, gs_x: l
     ax_top[-1].spines['right'].set_visible(False)
     ax_top[-1].spines['left'].set_visible(False)
     ax_top[-1].set_xticks(list(range(math.floor(df['start_speed'].min() / 5) * 5, math.ceil(df['start_speed'].max() / 5) * 5, 5)))
-    ax_top[-1].set_xlabel('Velocity (mph)')
+    ax_top[-1].set_xlabel('Velocity (mph)', fontdict=font_properties_axes)
 
 ### TJ STUFF+ ROLLING ###
 def tj_stuff_roling(df: pl.DataFrame, window: int, ax: plt.Axes):
@@ -370,6 +370,71 @@ def tj_stuff_roling_game(df: pl.DataFrame, window: int, ax: plt.Axes):
     ax.set_title(f"{window} Game Rolling tjStuff+", fontdict=font_properties_titles)
     ax.xaxis.set_major_locator(MaxNLocator(integer=True))
 
+
+def pitch_usage(df: pl.DataFrame, ax: plt.Axes):
+    """
+    Plot pitch usage as a population pyramid: left is % vs LHH, right is % vs RHH.
+
+    Parameters
+    ----------
+    df : pl.DataFrame
+        The DataFrame containing pitch data.
+    ax : plt.Axes
+        The axis to plot on.
+    """
+    # Get unique pitch types sorted by total usage
+    usage = (
+        df.group_by(['pitch_type', 'batter_hand'])
+          .agg(pl.col('is_pitch').sum().alias('count'))
+          .pivot(values='count', index='pitch_type', columns='batter_hand')
+          .fill_null(0)
+    )
+
+    # Calculate total pitches for each hand
+    total_lhh = usage['L'].sum() if 'L' in usage.columns else 0
+    total_rhh = usage['R'].sum() if 'R' in usage.columns else 0
+
+    # Calculate proportions (0-1), then scale to percent (0-100)
+    usage = usage.with_columns([
+        (pl.col('L') / total_lhh * 100).alias('L_prop') if total_lhh > 0 else pl.lit(0).alias('L_prop'),
+        (pl.col('R') / total_rhh * 100).alias('R_prop') if total_rhh > 0 else pl.lit(0).alias('R_prop')
+    ])
+
+    # Sort pitch types by total usage
+    usage = usage.with_columns(
+        (pl.col('L') + pl.col('R')).alias('total')
+    ).sort('total', descending=False)
+
+    pitch_types = usage['pitch_type'].to_list()
+    y_pos = np.arange(len(pitch_types))
+
+    # Plot bars: LHH to left (negative), RHH to right (positive)
+    ax.barh(y_pos, -usage['L_prop'].to_numpy(), color=[dict_colour.get(pt, '#888888') for pt in pitch_types], alpha=1, label='vs LHH')
+    ax.barh(y_pos, usage['R_prop'].to_numpy(), color=[dict_colour.get(pt, '#888888') for pt in pitch_types], alpha=1, label='vs RHH')
+
+    # Add counts on bars
+    for i, pt in enumerate(pitch_types):
+        if 'L' in usage.columns and usage['L'][i] > 0:
+            ax.text(-usage['L_prop'][i] - 4, i, f'{(usage["L_prop"][i]/100):.1%}', va='center', ha='right', fontsize=14)
+        if 'R' in usage.columns and usage['R'][i] > 0:
+            ax.text(usage['R_prop'][i] + 4, i, f'{(usage["R_prop"][i]/100):.1%}', va='center', ha='left', fontsize=14)
+
+    ax.set_yticks(y_pos)
+    ax.set_yticklabels([])
+    ax.set_xlabel('Usage (%)', fontdict=font_properties_axes)
+    ax.set_title('Pitch Usage', fontdict=font_properties_titles)
+    ax.axvline(0, color='black', linewidth=1)
+    # ax.get_legend().remove()
+    ax.set_xlim(-100, 100)
+    ax.set_xticks(np.arange(-100, 101, 20))
+    ax.set_xticklabels([f"{abs(x)}%" for x in np.arange(-100, 101, 20)], fontdict=font_properties)
+    # ax.grid(axis='x', linestyle='--', alpha=0.5)
+    ax.text(-98, -0.49, s='vs LHH', fontstyle='italic', ha='left', va='bottom',
+            bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
+    ax.text(98, -0.49, s='vs RHH', fontstyle='italic', ha='right', va='bottom',
+            bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
+
+
 def break_plot(df: pl.DataFrame, ax: plt.Axes):
     """
     Plot the pitch breaks for different pitch types.
@@ -433,15 +498,15 @@ def break_plot(df: pl.DataFrame, ax: plt.Axes):
     # Add text annotations for glove side and arm side
     if df['pitcher_hand'][0] == 'R':
         ax.text(-24.5, -24.5, s='← Glove Side', fontstyle='italic', ha='left', va='bottom',
-                bbox=dict(facecolor='white', edgecolor='black'), fontsize=12, zorder=3)
+                bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
         ax.text(24.5, -24.5, s='Arm Side →', fontstyle='italic', ha='right', va='bottom',
-                bbox=dict(facecolor='white', edgecolor='black'), fontsize=12, zorder=3)
+                bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
     elif df['pitcher_hand'][0] == 'L':
         ax.invert_xaxis()
         ax.text(24.5, -24.5, s='← Arm Side', fontstyle='italic', ha='left', va='bottom',
-                bbox=dict(facecolor='white', edgecolor='black'), fontsize=12, zorder=3)
+                bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
         ax.text(-24.5, -24.5, s='Glove Side →', fontstyle='italic', ha='right', va='bottom',
-                bbox=dict(facecolor='white', edgecolor='black'), fontsize=12, zorder=3)
+                bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
 
     # Set aspect ratio and format axis ticks
     ax.set_aspect('equal', adjustable='box')