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Understanding where speed changes occur on a circuit is crucial for lap analysis. By annotating a speed trace with corner locations, you can quickly identify braking zones, apex speeds, and acceleration patterns at specific corners. Speed trace with corner annotations

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Loading the session and data

We’ll analyze the fastest qualifying lap from the 2021 Spanish Grand Prix and overlay corner markers. 
import matplotlib.pyplot as plt
import tif1

# Enable plotting support
tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# Load qualifying session
session = tif1.get_session(2021, 'Spanish Grand Prix', 'Q')

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Getting the fastest lap telemetry

Select the fastest lap and retrieve its telemetry data with distance information. 
fastest_lap = session.laps.pick_fastest()
car_data = fastest_lap.get_car_data().add_distance()

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Loading circuit information

The circuit info contains corner locations, numbers, and other track-specific data. 
circuit_info = session.get_circuit_info()
The circuit_info.corners DataFrame includes:
  • Number: Corner number
  • Letter: Corner letter (for chicanes or complex sections)
  • Distance: Distance from start line in meters
  • Angle: Corner angle
  • X, Y: Track coordinates

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Creating the annotated plot

Plot the speed trace and add vertical lines at each corner location. 
# Get team color for the driver
team_color = tif1.plotting.get_team_color(fastest_lap['Team'], session=session)

# Create figure
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(car_data['Distance'], car_data['Speed'],
        color=team_color, label=fastest_lap['Driver'], linewidth=2)

# Draw vertical dotted lines at each corner
v_min = car_data['Speed'].min()
v_max = car_data['Speed'].max()
ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20,
          linestyles='dotted', colors='grey')

# Add corner numbers below the lines
for _, corner in circuit_info.corners.iterrows():
    txt = f"{corner['Number']}{corner['Letter']}"
    ax.text(corner['Distance'], v_min-30, txt,
            va='center_baseline', ha='center', size='small')

ax.set_xlabel('Distance (m)')
ax.set_ylabel('Speed (km/h)')
ax.legend()

# Adjust y-axis to include corner numbers
ax.set_ylim([v_min - 40, v_max + 20])

plt.suptitle(f"Speed Trace with Corner Annotations\n"
             f"{session.event['EventName']} {session.event.year} Qualifying")
plt.show()

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Understanding the visualization

The annotated speed trace reveals:
  • Corner entry speeds: Where the speed drops before each corner marker
  • Minimum corner speeds: The lowest point near each corner number
  • Corner exit acceleration: How quickly speed increases after each corner
  • Straight-line performance: Speed plateaus between corners
For example, Turn 1 at Barcelona typically shows a sharp speed drop from ~310 km/h to ~150 km/h, while the high-speed Turn 9 maintains speeds above 250 km/h.

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Comparing multiple drivers

You can extend this to compare multiple drivers by plotting additional speed traces: 
# Get laps from two drivers
lap1 = session.laps.pick_drivers('VER').pick_fastest()
lap2 = session.laps.pick_drivers('HAM').pick_fastest()

tel1 = lap1.get_car_data().add_distance()
tel2 = lap2.get_car_data().add_distance()

# Plot both with corner annotations
fig, ax = plt.subplots(figsize=(12, 6))

color1 = tif1.plotting.get_team_color(lap1['Team'], session=session)
color2 = tif1.plotting.get_team_color(lap2['Team'], session=session)

ax.plot(tel1['Distance'], tel1['Speed'], color=color1, label=lap1['Driver'], linewidth=2)
ax.plot(tel2['Distance'], tel2['Speed'], color=color2, label=lap2['Driver'], linewidth=2)

# Add corner markers
v_min = min(tel1['Speed'].min(), tel2['Speed'].min())
v_max = max(tel1['Speed'].max(), tel2['Speed'].max())
ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20,
          linestyles='dotted', colors='grey', alpha=0.5)

for _, corner in circuit_info.corners.iterrows():
    txt = f"{corner['Number']}{corner['Letter']}"
    ax.text(corner['Distance'], v_min-30, txt,
            va='center_baseline', ha='center', size='small')

ax.set_xlabel('Distance (m)')
ax.set_ylabel('Speed (km/h)')
ax.legend()
ax.set_ylim([v_min - 40, v_max + 20])

plt.show()

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Complete example

import matplotlib.pyplot as plt
import tif1

# Setup
tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# Load session
session = tif1.get_session(2021, 'Spanish Grand Prix', 'Q')

# Get fastest lap and telemetry
fastest_lap = session.laps.pick_fastest()
car_data = fastest_lap.get_car_data().add_distance()

# Get circuit info
circuit_info = session.get_circuit_info()

# Get team color
team_color = tif1.plotting.get_team_color(fastest_lap['Team'], session=session)

# Create plot
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(car_data['Distance'], car_data['Speed'],
        color=team_color, label=fastest_lap['Driver'], linewidth=2)

# Add corner annotations
v_min = car_data['Speed'].min()
v_max = car_data['Speed'].max()
ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20,
          linestyles='dotted', colors='grey')

for _, corner in circuit_info.corners.iterrows():
    txt = f"{corner['Number']}{corner['Letter']}"
    ax.text(corner['Distance'], v_min-30, txt,
            va='center_baseline', ha='center', size='small')

ax.set_xlabel('Distance (m)')
ax.set_ylabel('Speed (km/h)')
ax.legend()
ax.set_ylim([v_min - 40, v_max + 20])

plt.suptitle(f"Speed Trace with Corner Annotations\n"
             f"{session.event['EventName']} {session.event.year} Qualifying")
plt.show()

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Next steps

Last modified on March 6, 2026