Annotated Speed Trace

Identify where speed changes occur on a circuit. These locations have a large effect on lap time. Annotate a speed trace with corner locations to show braking zones, apex speeds, and acceleration patterns at specific corners.

Loading the Session and Data

Analyze the fastest qualifying lap from the 2021 Spanish Grand Prix. Overlay the 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')

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()

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

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()

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. The high-speed Turn 9 maintains speeds above 250 km/h.

Comparing Multiple Drivers

The native speed traces chart overlays the fastest laps of several drivers on a shared distance axis:

import tif1

# Overlay the fastest laps of two drivers
fig, ax = tif1.plot_speed_traces(2023, 'Spanish Grand Prix', 'Q', drivers=['VER', 'HAM'])
plt.show()

The telemetry comparison chart adds acceleration and driver-action panels.

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, fetches circuit info, and annotates the corners
fig, ax = tif1.plot_annotated_speed_trace(2021, 'Spanish Grand Prix', 'Q')
plt.show()

# Or save straight to a file
# tif1.plot_annotated_speed_trace(2021, 'Spanish Grand Prix', 'Q', save_path='annotated_speed_trace.png')

Next Steps

Explore Speed Traces Comparison for multi-driver analysis
Learn about Gear Shifts on Track for transmission analysis
Examine Telemetry Comparison for detailed channel analysis

​Loading the Session and Data

​Getting the Fastest Lap Telemetry

​Loading Circuit Information

​Creating the Annotated Plot

​Understanding the Visualization

​Comparing Multiple Drivers

​Complete Example

​Next Steps