Multi-Driver Speed Comparison

A speed comparison around a circuit identifies braking zones, acceleration areas, and flat-out sections. This tutorial shows how to create a color-coded track map. Color intensity along the racing line represents speed.

Setup and Configuration

First, set up the plotting environment with FastF1 color scheme and configure the session details.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import LineCollection
import tif1

# Setup plotting with FastF1 colors
tif1.plotting.setup_mpl(color_scheme='fastf1')

# Configuration
year = 2023
event = "Bahrain"
session_type = "Q"
driver = "VER"
colormap = mpl.cm.plasma

Loading Session and Telemetry

Load the session and extract telemetry data from the driver’s fastest lap.

# Load session
session = tif1.get_session(year, event, session_type)
weekend = session.event

# Get fastest lap telemetry
lap = session.laps.pick_driver(driver).pick_fastest()
telemetry = lap.get_car_data()

# Extract data for plotting
x = telemetry['X'].values
y = telemetry['Y'].values
color = telemetry['Speed'].values

Creating Line Segments

Convert the track coordinates into line segments for color mapping.

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

Building the Visualization

Create the plot with a background track line and color-coded speed overlay.

# Create visualization with FastF1 styling
fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(12, 6.75))
fig.suptitle(f'{weekend["EventName"]} {year} - {driver} - Speed',
             size=24, y=0.97, color='white')

# Adjust margins and turn off axis
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.12)
ax.axis('off')
ax.set_aspect('equal', adjustable='datalim')

# Create background track line
ax.plot(x, 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)

# Set the values used for colormapping
lc.set_array(color)

# Add line segments to plot
line = ax.add_collection(lc)

Adding the Color Bar

Add a horizontal color bar to show the speed scale.

# Create 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")
legend.set_label('Speed (km/h)', color='white', fontsize=12)
legend.ax.xaxis.set_tick_params(color='white')
plt.setp(plt.getp(legend.ax.axes, 'xticklabels'), color='white')

plt.show()

Parameters

The native chart overlays every selected driver on a single axes. A shared color scale keeps colors comparable across drivers. The chart includes a driver legend and a horizontal speed colorbar. Pick the drivers with the drivers list. The default is the top-3 finishers:

import tif1

# Overlay several drivers on one track
fig, ax = tif1.plot_multi_driver_speed_comparison(
    2023, 'Bahrain', 'Q',
    drivers=['VER', 'PER', 'LEC'],
    cmap='plasma',
    save_path='multi_driver_speed_comparison.png',
    dpi=300,
)
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_multi_driver_speed_comparison(2023, 'Bahrain', 'Q', drivers=['VER', 'PER', 'LEC'])
plt.show()

# Or save straight to a file
# tif1.plot_multi_driver_speed_comparison(2023, 'Bahrain', 'Q', save_path='multi_driver_speed_comparison.png')

Interpreting the Visualization

Red/yellow sections: High-speed areas like straights and fast corners
Blue/purple sections: Low-speed areas indicating braking zones and slow corners
Color transitions: Show acceleration and deceleration patterns
Track layout: Black background line provides context for the circuit shape

Alternative: Side-by-Side Subplots

The native chart overlays drivers on one axes. For a separate subplot per driver, use the manual approach:

drivers = ['VER', 'PER', 'LEC']
fig, axes = plt.subplots(1, len(drivers), figsize=(18, 6))

for idx, driver in enumerate(drivers):
    lap = session.laps.pick_driver(driver).pick_fastest()
    telemetry = lap.get_car_data()

    x = telemetry['X'].values
    y = telemetry['Y'].values
    speed = telemetry['Speed'].values

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

    axes[idx].plot(x, y, color='black', linewidth=16, zorder=0)

    norm = plt.Normalize(speed.min(), speed.max())
    lc = LineCollection(segments, cmap='plasma', norm=norm, linewidth=5)
    lc.set_array(speed)
    axes[idx].add_collection(lc)

    axes[idx].set_aspect('equal')
    axes[idx].axis('off')
    axes[idx].set_title(driver, color='white', fontsize=16)

plt.tight_layout()
plt.show()

Next Steps

Analyze lap time deltas for detailed time differences
Compare telemetry channels side by side
Visualize gear shifts to see transmission strategies

​Setup and Configuration

​Loading Session and Telemetry

​Creating Line Segments

​Building the Visualization

​Adding the Color Bar

​Parameters

​Complete Example

​Interpreting the Visualization

​Alternative: Side-by-Side Subplots

​Next Steps