Basketball Shot Analysis

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

# Basketball data
np.random.seed(42)
n_shots = 8000

distance = np.random.exponential(10, n_shots) + 3
distance = np.clip(distance, 0, 30)

shot_quality = np.random.beta(2, 2, n_shots) * 100

# Sports court theme
plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(10, 8))
fig.patch.set_facecolor('#0c0810')
ax.set_facecolor('#0c0810')

# Custom colormap - basketball orange
colors = ['#0c0810', '#181020', '#241830', '#302040', '#402850', 
          '#603060', '#803870', '#a04080', '#c05090', '#e070a0', '#ff90b0']
cmap = LinearSegmentedColormap.from_list('basketball', colors, N=256)

# 2D histogram
h = ax.hist2d(distance, shot_quality, bins=[40, 40], cmap=cmap, cmin=1)

# 3-point line
ax.axvline(x=23.75, color='#ff6b35', linestyle='--', alpha=0.8, linewidth=2.5, label='3-Point Line')
ax.axvline(x=15, color='#ffa502', linestyle=':', alpha=0.6, linewidth=1.5, label='Mid-Range')

# Hot zone
from matplotlib.patches import Rectangle
hot = Rectangle((0, 70), 8, 30, fill=False, edgecolor='#ff90b0', 
                linewidth=2.5, linestyle='-', alpha=0.8, label='Hot Zone')
ax.add_patch(hot)

# Styled colorbar
cbar = plt.colorbar(h[3], ax=ax, pad=0.02, shrink=0.85)
cbar.ax.set_facecolor('#0c0810')
cbar.set_label('Shot Attempts', fontsize=11, color='#e0a0c0', labelpad=10)
cbar.ax.yaxis.set_tick_params(color='#e0a0c0')
cbar.outline.set_edgecolor('#402040')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#e0a0c0', fontsize=9)

ax.set_xlabel('Shot Distance (feet)', fontsize=13, color='#e0a0c0', fontweight='600', labelpad=10)
ax.set_ylabel('Shot Quality Score', fontsize=13, color='#e0a0c0', fontweight='600', labelpad=10)
ax.set_title('NBA Shot Distribution Analysis', fontsize=16, color='white', fontweight='bold', pad=20)

ax.tick_params(colors='#c080a0', labelsize=10, length=0)
for spine in ax.spines.values():
    spine.set_visible(False)

ax.legend(loc='upper right', fontsize=9, facecolor='#1c1020', edgecolor='#402040', labelcolor='#e0a0c0')
plt.tight_layout()
plt.show()