Track Brake Zones

Brake zone visualization highlights where drivers apply the brakes around the circuit. This is crucial for understanding corner entry, comparing braking points, and analyzing overtaking opportunities. Track brake zones showing where drivers brake

Loading the session

import tif1
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

# Load session
session = tif1.get_session(2023, 'Monza', 'Q')
fastest_lap = session.laps.pick_fastest()

Getting brake data

Brake data is binary: 0 (not braking) or 1 (braking).

# Get telemetry
telemetry = fastest_lap.get_car_data()

# Extract position and brake
x = telemetry['X']
y = telemetry['Y']
brake = telemetry['Brake']

Creating the brake zone map

# Prepare segments
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)

# Create plot
fig, ax = plt.subplots(figsize=(12, 10))

# Color-coded by brake (binary)
norm = plt.Normalize(0, 1)
lc = LineCollection(segments, cmap='RdYlGn_r', norm=norm, linewidth=4)
lc.set_array(brake)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Brake', ticks=[0, 1])
cbar.ax.set_yticklabels(['Off', 'On'])

# Format
ax.set_aspect('equal')
ax.axis('off')
plt.suptitle(f"{session.event['EventName']} {session.event.year} - Brake Zones")
plt.tight_layout()
plt.show()

Complete example

import tif1
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

# Load session
session = tif1.get_session(2023, 'Monza', 'Q')
fastest_lap = session.laps.pick_fastest()

# Get telemetry
telemetry = fastest_lap.get_car_data()
x = telemetry['X']
y = telemetry['Y']
brake = telemetry['Brake']

# Prepare segments
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)

# Create plot
fig, ax = plt.subplots(figsize=(12, 10))

# Color-coded track
norm = plt.Normalize(0, 1)
lc = LineCollection(segments, cmap='RdYlGn_r', norm=norm, linewidth=4)
lc.set_array(brake)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Brake', ticks=[0, 1])
cbar.ax.set_yticklabels(['Off', 'On'])

# Format
ax.set_aspect('equal')
ax.axis('off')
plt.suptitle(f"{session.event['EventName']} {session.event.year} - Brake Zones")
plt.tight_layout()
plt.show()

Analyzing brake zones

Red sections: Active braking zones before corners
Green sections: No braking (straights and corners)
Length of red zones: Indicates braking duration and intensity

Circuits like Monza have fewer but more intense braking zones, while Monaco has frequent braking throughout the lap.

Comparing drivers

To compare braking points between drivers:

# Get two laps
lap1 = session.laps.pick_drivers('VER').pick_fastest()
lap2 = session.laps.pick_drivers('HAM').pick_fastest()

# Plot both on same figure
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))

# Plot lap 1 on ax1, lap 2 on ax2
# (use same code as above for each)

Next steps

Combine with speed maps to see braking effectiveness
Analyze throttle application after braking
Study longitudinal acceleration during braking

​Loading the session

​Getting brake data

​Creating the brake zone map

​Complete example

​Analyzing brake zones

​Comparing drivers

​Next steps