Telemetry Analysis Examples

Analyze driver telemetry data including speed traces, GPS positions, and gear shifts

FastF1 provides comprehensive telemetry data for detailed analysis of driver performance. These examples demonstrate how to visualize and compare telemetry data.

Speed Traces

Compare two drivers’ fastest laps by overlaying their speed traces throughout the lap.

import matplotlib.pyplot as plt
import fastf1.plotting

# Enable Matplotlib patches for plotting timedelta values and load
# FastF1's dark color scheme
fastf1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# load a session and its telemetry data
session = fastf1.get_session(2021, 'Spanish Grand Prix', 'Q')
session.load()

# Select the two laps that we want to compare
ver_lap = session.laps.pick_drivers('VER').pick_fastest()
ham_lap = session.laps.pick_drivers('HAM').pick_fastest()

# Get the telemetry data for each lap and add distance column
ver_tel = ver_lap.get_car_data().add_distance()
ham_tel = ham_lap.get_car_data().add_distance()

# Create the plot with team colors
rbr_color = fastf1.plotting.get_team_color(ver_lap['Team'], session=session)
mer_color = fastf1.plotting.get_team_color(ham_lap['Team'], session=session)

fig, ax = plt.subplots()
ax.plot(ver_tel['Distance'], ver_tel['Speed'], color=rbr_color, label='VER')
ax.plot(ham_tel['Distance'], ham_tel['Speed'], color=mer_color, label='HAM')

ax.set_xlabel('Distance in m')
ax.set_ylabel('Speed in km/h')
ax.legend()
plt.suptitle(f"Fastest Lap Comparison \n "
             f"{session.event['EventName']} {session.event.year} Qualifying")

plt.show()

What This Example Shows

• Loading qualifying session data
• Selecting fastest laps for specific drivers
• Extracting and adding distance data to telemetry
• Using team colors for styling
• Plotting multiple speed traces for comparison

Expected Output

A line plot showing speed (km/h) vs distance (m) for both drivers, with team-colored lines showing where each driver brakes, accelerates, and reaches top speed.

Speed Visualization on Track

Visualize speed variations on the track map using a color gradient.

import matplotlib as mpl
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import fastf1 as ff1

# Define variables
year = 2021
wknd = 9
ses = 'R'
driver = 'RIC'
colormap = mpl.cm.plasma

# Load the session and select the desired data
session = ff1.get_session(year, wknd, ses)
weekend = session.event
session.load()
lap = session.laps.pick_drivers(driver).pick_fastest()

# Get telemetry data
x = lap.telemetry['X']              # values for x-axis
y = lap.telemetry['Y']              # values for y-axis
color = lap.telemetry['Speed']      # value to base color gradient on

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

# Create the plot
fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(12, 6.75))
fig.suptitle(f'{weekend.name} {year} - {driver} - Speed', size=24, y=0.97)

plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.12)
ax.axis('off')

# Create background track line
ax.plot(lap.telemetry['X'], lap.telemetry['Y'],
        color='black', linestyle='-', linewidth=16, zorder=0)

# Create a continuous norm to map from data points to colors
norm = plt.Normalize(color.min(), color.max())
lc = LineCollection(segments, cmap=colormap, norm=norm,
                    linestyle='-', linewidth=5)
lc.set_array(color)
line = ax.add_collection(lc)

# Add color bar as legend
cbaxes = fig.add_axes([0.25, 0.05, 0.5, 0.05])
normlegend = mpl.colors.Normalize(vmin=color.min(), vmax=color.max())
legend = mpl.colorbar.ColorbarBase(cbaxes, norm=normlegend, cmap=colormap,
                                   orientation="horizontal")

plt.show()

What This Example Shows

• Loading GPS coordinates (X, Y) from telemetry
• Creating colored line segments based on speed
• Using LineCollection for gradient coloring
• Creating a track map visualization
• Adding a color bar legend

Expected Output

A track map showing the circuit layout colored by speed, with slower sections (braking zones) in one color and faster sections (straights) in another.

Corner Annotations

Annotate speed traces with corner numbers and positions.

import matplotlib.pyplot as plt
import fastf1.plotting

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

# Load session data
session = fastf1.get_session(2021, 'Spanish Grand Prix', 'Q')
session.load()

# Select the fastest lap and get car telemetry
fastest_lap = session.laps.pick_fastest()
car_data = fastest_lap.get_car_data().add_distance()

# Load circuit info with corner locations
circuit_info = session.get_circuit_info()

# Create the plot
team_color = fastf1.plotting.get_team_color(fastest_lap['Team'],
                                            session=session)

fig, ax = plt.subplots()
ax.plot(car_data['Distance'], car_data['Speed'],
        color=team_color, label=fastest_lap['Driver'])

# Draw vertical 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')

# Plot corner numbers
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 in m')
ax.set_ylabel('Speed in km/h')
ax.legend()
ax.set_ylim([v_min - 40, v_max + 20])

plt.show()

What This Example Shows

• Using get_circuit_info() to access corner data
• Plotting vertical lines at corner positions
• Adding text annotations for corner numbers
• Adjusting plot limits to accommodate annotations

Expected Output

A speed trace with vertical dotted lines marking each corner and numbered labels showing which corner is which.

Gear Shifts

Visualize which gear is being used at each point on the track.

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colormaps
from matplotlib.collections import LineCollection
import fastf1

# Load session data
session = fastf1.get_session(2021, 'Austrian Grand Prix', 'Q')
session.load()

lap = session.laps.pick_fastest()
tel = lap.get_telemetry()

# Prepare data for plotting
x = np.array(tel['X'].values)
y = np.array(tel['Y'].values)

points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
gear = tel['nGear'].to_numpy().astype(float)

# Create line collection with segmented colormap
cmap = colormaps['Paired']
lc_comp = LineCollection(segments, norm=plt.Normalize(1, cmap.N+1), cmap=cmap)
lc_comp.set_array(gear)
lc_comp.set_linewidth(4)

# Create the plot
plt.gca().add_collection(lc_comp)
plt.axis('equal')
plt.tick_params(labelleft=False, left=False, labelbottom=False, bottom=False)

title = plt.suptitle(
    f"Fastest Lap Gear Shift Visualization\n"
    f"{lap['Driver']} - {session.event['EventName']} {session.event.year}"
)

# Add colorbar with gear numbers
cbar = plt.colorbar(mappable=lc_comp, label="Gear",
                    boundaries=np.arange(1, 10))
cbar.set_ticks(np.arange(1.5, 9.5))
cbar.set_ticklabels(np.arange(1, 9))

plt.show()

What This Example Shows

• Accessing gear data (nGear) from telemetry
• Creating a segmented colormap for discrete values
• Plotting gear usage on the track map
• Customizing colorbar ticks for discrete data

Expected Output

A track map colored by gear number, showing where drivers use each gear throughout the lap. Low gears appear in corners, high gears on straights.

Key Concepts

Telemetry Data Fields

FastF1 provides access to various telemetry channels:

• Speed: Speed in km/h
• RPM: Engine RPM
• nGear: Current gear (1-8)
• Throttle: Throttle position (0-100%)
• Brake: Brake pressure
• DRS: DRS status
• X, Y, Z: GPS coordinates

Adding Distance

The add_distance() method calculates the distance traveled from the start of the lap, making it easier to align data from different laps.

Circuit Information

The get_circuit_info() method provides:

• Corner locations and numbers
• Track rotation for proper map orientation
• Circuit-specific metadata

Telemetry data is only available for sessions from 2018 onwards.