Soccer Player Movement Analysis

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(456)

# Soccer player tracking data (90 min match)
n_positions = 250

# Simulate player movement patterns
t = np.linspace(0, 90, n_positions)

# Player position on field
x = 50 + 30 * np.sin(t / 15) + np.random.normal(0, 5, n_positions)  # Field width
y = 35 + 25 * np.sin(t / 10 + 1) + np.random.normal(0, 8, n_positions)  # Field length

# Speed (z-axis)
speed = np.abs(np.diff(np.sqrt(np.diff(x)**2 + np.diff(y)**2)))
speed = np.concatenate([[0, 0], speed])
speed = speed * 10 + np.random.exponential(2, n_positions)

fig = plt.figure(figsize=(10, 8), facecolor='#ffffff')
ax = fig.add_subplot(111, projection='3d', facecolor='#ffffff')

scatter = ax.scatter(x, y, t, c=speed, cmap='YlOrRd', s=speed*3+10, 
                     alpha=0.7, edgecolors='#374151', linewidths=0.3)

cbar = plt.colorbar(scatter, ax=ax, shrink=0.6, pad=0.1)
cbar.set_label('Speed (km/h)', color='#1f2937', fontsize=10)
cbar.ax.tick_params(colors='#6b7280')

ax.set_xlabel('Field X (m)', color='#1f2937', fontsize=10)
ax.set_ylabel('Field Y (m)', color='#1f2937', fontsize=10)
ax.set_zlabel('Time (min)', color='#1f2937', fontsize=10)
ax.set_title('Soccer Player Movement Analysis', color='#1f2937', fontsize=14, fontweight='bold', pad=20)

ax.tick_params(colors='#6b7280', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#e5e7eb')
ax.yaxis.pane.set_edgecolor('#e5e7eb')
ax.zaxis.pane.set_edgecolor('#e5e7eb')

ax.view_init(elev=20, azim=45)
plt.tight_layout()
plt.show()