Top Speeds by Team

Speed trap data reveals which teams have the most powerful engines and efficient aerodynamics. Compare maximum speeds across teams to identify performance advantages on straights and understand the power unit hierarchy.

Loading the Session

Analyze speed trap data from the 2023 Italian Grand Prix qualifying session. Top speeds are typically highest at Monza because of the low-downforce setup.

import tif1
import matplotlib.pyplot as plt
import pandas as pd

# Load qualifying session
session = tif1.get_session(2023, 'Italian Grand Prix', 'Q')

Finding the Fastest Speed Trap

F1 tracks have multiple speed measurement points (I1, I2, ST, FL). Identify which one recorded the highest speeds.

# Get speed columns
speed_columns = ['SpeedI1', 'SpeedI2', 'SpeedST', 'SpeedFL']
speed_data = session.laps[speed_columns]

# Find which speed trap had the highest readings
fastest_trap = speed_data.idxmax(axis=1).value_counts().index[0]
print(f"Fastest speed trap: {fastest_trap}")

Calculating Maximum Speeds by Team

Group the data by team and find the maximum speed recorded for each.

# Prepare data with team information
team_speeds = session.laps[[fastest_trap, 'Team']].copy()

# Get maximum speed per team
max_speeds = team_speeds.groupby('Team')[fastest_trap].max().reset_index()
max_speeds.columns = ['Team', 'MaxSpeed']

# Sort by speed
max_speeds = max_speeds.sort_values('MaxSpeed', ascending=False)

# Calculate difference from slowest
max_speeds['Diff'] = max_speeds['MaxSpeed'] - max_speeds['MaxSpeed'].min()

Adding Team Colors

Use the plotting module to get official team colors for the visualization.

# Get team colors
team_colors = {}
for team in max_speeds['Team']:
    team_colors[team] = tif1.plotting.get_team_color(team, session=session)

max_speeds['Color'] = max_speeds['Team'].map(team_colors)

Creating the Visualization

Build a horizontal bar chart showing the speed differences between teams.

fig, ax = plt.subplots(figsize=(10, 8))

# Create horizontal bars
bars = ax.barh(
    y=max_speeds['Team'],
    width=max_speeds['Diff'],
    color=max_speeds['Color'],
    height=0.7
)

# Add speed labels
for i, (speed, diff) in enumerate(zip(max_speeds['MaxSpeed'], max_speeds['Diff'])):
    ax.text(
        diff + 0.2,
        i,
        f"{int(speed)} km/h",
        va='center',
        fontsize=10,
        fontweight='bold'
    )

# Styling
ax.set_xlabel('Speed Difference (km/h)', fontsize=11)
ax.set_title(
    f"{session.event.year} {session.event['EventName']} - Top Speeds by Team",
    fontsize=13,
    fontweight='bold',
    pad=20
)
ax.invert_yaxis()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

Interpreting the Results

When analyzing top speed data, consider:

Power unit performance: Teams with the same engine supplier often show similar top speeds
Aerodynamic efficiency: Lower drag setups achieve higher speeds but may sacrifice cornering performance
Track-specific setups: Monza and Spa typically show the highest speeds due to low-downforce configurations
DRS effect: Ensure the comparison is like-for-like (DRS enabled or disabled)

Parameters

The native chart exposes the shared filters. Restrict the comparison with teams, or force a specific trap with speed_trap:

import tif1

# Compare selected teams at a chosen speed trap
fig, ax = tif1.plot_top_speeds(
    2023, 'Italian Grand Prix', 'Q',
    teams=['Ferrari', 'Red Bull Racing', 'Mercedes'],
    speed_trap='SpeedST',
)
plt.show()

The default speed_trap=None auto-detects the trap with the highest readings, using exactly the logic from the sections above. Omit teams (the default) to include every team.

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, detects the fastest trap, and plots team maxima
fig, ax = tif1.plot_top_speeds(2023, 'Italian Grand Prix', 'Q')
plt.show()

# Or save straight to a file
# tif1.plot_top_speeds(2023, 'Italian Grand Prix', 'Q', save_path='top_speeds.png')

Next Steps

Compare speed trap data across different tracks to see setup variations
Analyze the relationship between top speed and lap time
Explore telemetry comparison for detailed speed profiles

​Loading the Session

​Finding the Fastest Speed Trap

​Calculating Maximum Speeds by Team

​Adding Team Colors

​Creating the Visualization

​Interpreting the Results

​Parameters

​Complete Example

​Next Steps