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Speed traces are one of the most intuitive ways to compare lap performance between drivers. By overlaying the speed profiles of two laps, you can instantly see where one driver is faster and identify braking points, acceleration zones, and top speeds. Speed traces comparison between two drivers

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Loading the session

We’ll compare the fastest qualifying laps from the 2023 Spanish Grand Prix between Verstappen and Hamilton. 
import tif1
import matplotlib.pyplot as plt

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

# Load qualifying session
session = tif1.get_session(2023, 'Spanish Grand Prix', 'Q')

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Selecting the laps

Use the pick_drivers() and pick_fastest() methods to get each driver’s fastest lap. 
ver_lap = session.laps.pick_drivers('VER').pick_fastest()
ham_lap = session.laps.pick_drivers('HAM').pick_fastest()

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Getting telemetry with distance

Telemetry data includes speed, throttle, brake, and more. The add_distance() method adds a cumulative distance column, making it easy to align laps for comparison. 
ver_tel = ver_lap.get_car_data().add_distance()
ham_tel = ham_lap.get_car_data().add_distance()

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Plotting with team colors

Use tif1.plotting.get_team_color() to automatically fetch the correct team colors for visual consistency. 
# Get team colors
rbr_color = tif1.plotting.get_team_color(ver_lap['Team'], session=session)
mer_color = tif1.plotting.get_team_color(ham_lap['Team'], session=session)

# Create the plot
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(ver_tel['Distance'], ver_tel['Speed'], color=rbr_color, label='VER', linewidth=2)
ax.plot(ham_tel['Distance'], ham_tel['Speed'], color=mer_color, label='HAM', linewidth=2)

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

plt.suptitle(f"Fastest Lap Speed Comparison\n"
             f"{session.event['EventName']} {session.event.year} Qualifying")
plt.show()

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Analyzing the traces

When examining speed traces, look for:
  • Braking zones: Sharp drops in speed indicate braking points. Earlier or later braking can reveal different driving styles or car characteristics.
  • Minimum corner speeds: The lowest point in each dip shows how much speed is carried through corners.
  • Acceleration: The steepness of the speed increase after corners indicates traction and power delivery.
  • Top speeds: Peak values on straights reveal engine power and drag levels.

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Complete example

import tif1
import matplotlib.pyplot as plt

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

# Load session
session = tif1.get_session(2023, 'Spanish Grand Prix', 'Q')

# Get fastest laps
ver_lap = session.laps.pick_drivers('VER').pick_fastest()
ham_lap = session.laps.pick_drivers('HAM').pick_fastest()

# Get telemetry with distance
ver_tel = ver_lap.get_car_data().add_distance()
ham_tel = ham_lap.get_car_data().add_distance()

# Get team colors
rbr_color = tif1.plotting.get_team_color(ver_lap['Team'], session=session)
mer_color = tif1.plotting.get_team_color(ham_lap['Team'], session=session)

# Plot
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(ver_tel['Distance'], ver_tel['Speed'], color=rbr_color, label='VER', linewidth=2)
ax.plot(ham_tel['Distance'], ham_tel['Speed'], color=mer_color, label='HAM', linewidth=2)

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

plt.suptitle(f"Fastest Lap Speed Comparison\n"
             f"{session.event['EventName']} {session.event.year} Qualifying")
plt.show()

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Next steps

  • Explore other telemetry channels like throttle and brake in the Telemetry Deep Dive
  • Learn about calculating time deltas between laps
  • Analyze gear shifts and RPM data for transmission insights
Last modified on March 6, 2026