Visualizing gear usage across a lap provides insight into corner characteristics, acceleration zones, and the technical demands of a circuit. This tutorial shows how to create a color-coded track map where each segment is colored by the gear being used.
Loading the session
We’ll analyze the fastest qualifying lap from the 2021 Austrian Grand Prix.
import tif1
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colormaps
from matplotlib.collections import LineCollection
# Enable plotting support
tif1.plotting.setup_mpl(mpl_timedelta_support=False, color_scheme='fastf1')
# Load qualifying session
session = tif1.get_session(2021, 'Austrian Grand Prix', 'Q')
Getting telemetry data
Extract the fastest lap and its telemetry, which includes X/Y coordinates and gear data.
lap = session.laps.pick_fastest()
tel = lap.get_telemetry()
Preparing the data
Convert telemetry to numpy arrays and create line segments for plotting.
# Extract X and Y coordinates
x = np.array(tel['X'].values)
y = np.array(tel['Y'].values)
# Create line segments from consecutive points
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
# Get gear data as float array
gear = tel['nGear'].to_numpy().astype(float)
Creating the visualization
Use matplotlib’s LineCollection to color each segment by gear.
# Create a 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
fig, ax = plt.subplots(figsize=(10, 8))
ax.add_collection(lc_comp)
ax.axis('equal')
ax.tick_params(labelleft=False, left=False, labelbottom=False, bottom=False)
plt.suptitle(
f"Fastest Lap Gear Shift Visualization\n"
f"{lap['Driver']} - {session.event['EventName']} {session.event.year}"
)
Adding a colorbar
Add a colorbar to show which color corresponds to each gear.
# Add colorbar with centered ticks for each gear
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()
Analyzing gear usage
When examining gear shift patterns, look for:
- Low-speed corners: Gears 1-3 indicate tight corners requiring heavy braking
- Medium-speed corners: Gears 4-5 show flowing sections where momentum is maintained
- High-speed sections: Gears 6-8 reveal straights and fast corners
- Shift points: Transitions between colors show where drivers change gears
Different circuits have distinct gear usage profiles. Street circuits like Monaco use lower gears extensively, while high-speed tracks like Monza spend more time in top gears.
Complete example
import tif1
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colormaps
from matplotlib.collections import LineCollection
# Setup
tif1.plotting.setup_mpl(mpl_timedelta_support=False, color_scheme='fastf1')
# Load session
session = tif1.get_session(2021, 'Austrian Grand Prix', 'Q')
# Get fastest lap and telemetry
lap = session.laps.pick_fastest()
tel = lap.get_telemetry()
# Prepare data
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
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)
# Plot
fig, ax = plt.subplots(figsize=(10, 8))
ax.add_collection(lc_comp)
ax.axis('equal')
ax.tick_params(labelleft=False, left=False, labelbottom=False, bottom=False)
plt.suptitle(
f"Fastest Lap Gear Shift Visualization\n"
f"{lap['Driver']} - {session.event['EventName']} {session.event.year}"
)
# Add colorbar
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()
Next steps
- Combine gear data with throttle/brake to analyze driving technique
- Compare gear usage between different drivers on the same circuit
- Analyze how gear usage changes between qualifying and race conditions
- Explore other track visualizations like speed or throttle maps
Last modified on March 6, 2026
