Speed Traces Comparison

Speed traces give a direct comparison of lap performance between drivers. Overlay the speed profiles of two laps. The overlay shows where one driver is faster. It also shows braking points, acceleration zones, and top speeds.

Loading the Session

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')

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()

Getting Telemetry with Distance

Telemetry data includes speed, throttle, brake, and more. The add_distance() method adds a cumulative distance column. Use this column to align the laps for comparison.

ver_tel = ver_lap.get_car_data().add_distance()
ham_tel = ham_lap.get_car_data().add_distance()

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()

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.

Parameters

The native chart accepts the shared filters. The drivers list selects which fastest laps to overlay; the default is the top-2 finishers:

import tif1

# Compare any set of drivers
fig, ax = tif1.plot_speed_traces(
    2023, 'Spanish Grand Prix', 'Q',
    drivers=['VER', 'HAM', 'LEC'],
    save_path='speed_traces.png',
    dpi=150,
)
plt.show()

Complete Example

The whole workflow above is wrapped in a single native function:

import tif1
import matplotlib.pyplot as plt

# One call: loads the session and overlays each driver's fastest lap
fig, ax = tif1.plot_speed_traces(2023, 'Spanish Grand Prix', 'Q', drivers=['VER', 'HAM'])
plt.show()

# Or save straight to a file
# tif1.plot_speed_traces(2023, 'Spanish Grand Prix', 'Q', save_path='speed_traces.png')

Next Steps

Explore other telemetry channels like throttle and brake in the Telemetry Comparison
Learn about calculating time deltas between laps
Analyze gear shifts and RPM data for transmission insights

​Loading the Session

​Selecting the Laps

​Getting Telemetry with Distance

​Plotting with Team Colors

​Analyzing the Traces

​Parameters

​Complete Example

​Next Steps