Gear Shifts on Track

Gear usage across a lap shows corner characteristics, acceleration zones, and the technical demands of a circuit. This tutorial creates a color-coded track map. Each segment is colored by the gear in use.

Gear shifts visualization on track layout

Loading the Session

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 the matplotlib 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.

Parameters

The native chart accepts the shared filters plus a few chart-specific options. Change the gear color scale with cmap, or write the figure straight to a file with save_path:

import tif1

# Custom colormap, saved directly to a file
fig, ax = tif1.plot_gear_shifts(
    2021, 'Austrian Grand Prix', 'Q',
    cmap='tab20',
    save_path='gear_shifts_on_track.png',
    dpi=150,
)
plt.show()

The colorbar is scaled from the data, not the fixed 8-gear range in the example above. The chart works for any gearbox.

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 colors the track by gear
fig, ax = tif1.plot_gear_shifts(2021, 'Austrian Grand Prix', 'Q')
plt.show()

# Or save straight to a file
# tif1.plot_gear_shifts(2021, 'Austrian Grand Prix', 'Q', save_path='gear_shifts_on_track.png')

Next Steps

Combine gear data with throttle and brake data 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

​Loading the Session

​Getting Telemetry Data

​Preparing the Data

​Creating the Visualization

​Adding a Colorbar

​Analyzing Gear Usage

​Parameters

​Complete Example

​Next Steps