Full Throttle Distance Analysis

Understanding how much of a lap drivers spend at full throttle reveals track characteristics and driving styles. Power-limited tracks like Monza show high percentages, while technical circuits like Monaco show much lower values.

Full throttle distance comparison across drivers

The concept

The full throttle metric calculates what percentage of the lap distance is covered with the throttle at or above 98%:

Full Throttle % = (Distance at ≥98% throttle / Total lap distance) × 100

Higher values indicate power-limited tracks with long straights
Lower values indicate technical tracks with many corners
Differences between drivers can reveal confidence, car balance, or setup choices

Loading the session

We’ll analyze qualifying from the 2023 Bahrain Grand Prix, a track with a good mix of straights and technical sections.

import tif1
import matplotlib.pyplot as plt

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

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

Calculating throttle distance

For each driver, we analyze their fastest lap telemetry to find where throttle is at or above 98%.

drivers = session.laps['Driver'].unique()
throttle_data = []

for driver in drivers:
    try:
        # Get fastest lap
        fastest_lap = session.laps.pick_drivers(driver).pick_fastest()
        telemetry = fastest_lap.get_car_data().add_distance()

        # Calculate distance between each telemetry point
        telemetry['Distance_delta'] = telemetry['Distance'].diff()

        # Get total circuit length
        circuit_length = telemetry['Distance'].max()

        # Filter for full throttle (≥98%)
        full_throttle = telemetry[telemetry['Throttle'] >= 98]

        # Sum up distance at full throttle
        throttle_distance = full_throttle['Distance_delta'].sum()
        throttle_percentage = (throttle_distance / circuit_length) * 100

        throttle_data.append({
            'Driver': driver,
            'Team': fastest_lap['Team'],
            'ThrottlePercentage': round(throttle_percentage, 2)
        })
    except Exception:
        continue

# Sort by throttle percentage
throttle_data.sort(key=lambda x: x['ThrottlePercentage'], reverse=True)

Visualizing the results

Create a horizontal bar chart showing relative differences between drivers.

# Map to minimum value to show relative differences
min_percentage = min(d['ThrottlePercentage'] for d in throttle_data)
for d in throttle_data:
    d['PercentageDiff'] = d['ThrottlePercentage'] - min_percentage

fig, ax = plt.subplots(figsize=(12, 8))

drivers_list = [d['Driver'] for d in throttle_data]
percentages_diff = [d['PercentageDiff'] for d in throttle_data]
percentages_actual = [d['ThrottlePercentage'] for d in throttle_data]
colors = [tif1.plotting.get_team_color(d['Team'], session) for d in throttle_data]

# Create bars with relative differences
bars = ax.barh(drivers_list, percentages_diff, color=colors, alpha=0.8,
               edgecolor='white', linewidth=1)

# Add actual percentage labels
for i, (driver, percentage) in enumerate(zip(drivers_list, percentages_actual)):
    ax.text(percentages_diff[i] + 0.1, i, f'{percentage:.1f}%',
            va='center', fontsize=10, fontweight='bold')

# Styling
ax.set_xlabel('Distance at Full Throttle (relative difference)',
              fontsize=12, fontweight='bold')
ax.set_ylabel('Driver', fontsize=12, fontweight='bold')
ax.set_title('Bahrain GP Qualifying - Full Throttle Distance',
             fontsize=14, fontweight='bold')
ax.invert_yaxis()
ax.grid(axis='x', alpha=0.3, linestyle='--')
ax.set_xlim(left=0)

plt.tight_layout()
plt.show()

The chart displays relative differences (bar lengths) while showing actual percentages (labels), making it easy to compare drivers.

Interpreting the results

When analyzing full throttle distance, consider:

Track layout: Monza (~70%) vs Monaco (~30%) shows dramatic differences
Driver confidence: Higher percentages can indicate confidence in car balance
Setup choices: Aggressive setups may allow earlier throttle application
Tire management: Drivers managing tires may be more conservative with throttle

Comparing across tracks

You can compare the same driver across different tracks to understand track characteristics:

tracks = ['Bahrain Grand Prix', 'Monaco Grand Prix', 'Italian Grand Prix']
driver = 'VER'
track_comparison = []

for track in tracks:
    try:
        session = tif1.get_session(2023, track, 'Q')
        fastest_lap = session.laps.pick_drivers(driver).pick_fastest()
        telemetry = fastest_lap.get_car_data().add_distance()

        telemetry['Distance_delta'] = telemetry['Distance'].diff()
        circuit_length = telemetry['Distance'].max()
        full_throttle = telemetry[telemetry['Throttle'] >= 98]
        throttle_distance = full_throttle['Distance_delta'].sum()
        throttle_percentage = (throttle_distance / circuit_length) * 100

        track_comparison.append({
            'Track': track,
            'ThrottlePercentage': round(throttle_percentage, 2)
        })
    except Exception:
        continue

# Plot comparison
fig, ax = plt.subplots(figsize=(10, 6))
tracks_list = [t['Track'] for t in track_comparison]
percentages = [t['ThrottlePercentage'] for t in track_comparison]

ax.bar(tracks_list, percentages, color='#1E88E5', alpha=0.8, edgecolor='white', linewidth=2)

for i, percentage in enumerate(percentages):
    ax.text(i, percentage + 1, f'{percentage:.1f}%', ha='center', fontsize=11, fontweight='bold')

ax.set_ylabel('Full Throttle Distance (%)', fontsize=12, fontweight='bold')
ax.set_title(f'{driver} - Full Throttle Distance Across Tracks', fontsize=14, fontweight='bold')
ax.grid(axis='y', alpha=0.3)
ax.set_ylim(bottom=0)

plt.xticks(rotation=15, ha='right')
plt.tight_layout()
plt.show()

Complete example

import tif1
import matplotlib.pyplot as plt

# Setup
tif1.plotting.setup_mpl(color_scheme='fastf1')
session = tif1.get_session(2023, 'Bahrain Grand Prix', 'Q')

# Calculate throttle distance
drivers = session.laps['Driver'].unique()
throttle_data = []

for driver in drivers:
    try:
        fastest_lap = session.laps.pick_drivers(driver).pick_fastest()
        telemetry = fastest_lap.get_car_data().add_distance()
        telemetry['Distance_delta'] = telemetry['Distance'].diff()

        circuit_length = telemetry['Distance'].max()
        full_throttle = telemetry[telemetry['Throttle'] >= 98]
        throttle_distance = full_throttle['Distance_delta'].sum()
        throttle_percentage = (throttle_distance / circuit_length) * 100

        throttle_data.append({
            'Driver': driver,
            'Team': fastest_lap['Team'],
            'ThrottlePercentage': round(throttle_percentage, 2)
        })
    except Exception:
        continue

throttle_data.sort(key=lambda x: x['ThrottlePercentage'], reverse=True)

# Map to minimum for visualization
min_percentage = min(d['ThrottlePercentage'] for d in throttle_data)
for d in throttle_data:
    d['PercentageDiff'] = d['ThrottlePercentage'] - min_percentage

# Plot
fig, ax = plt.subplots(figsize=(12, 8))
drivers_list = [d['Driver'] for d in throttle_data]
percentages_diff = [d['PercentageDiff'] for d in throttle_data]
percentages_actual = [d['ThrottlePercentage'] for d in throttle_data]
colors = [tif1.plotting.get_team_color(d['Team'], session) for d in throttle_data]

ax.barh(drivers_list, percentages_diff, color=colors, alpha=0.8, edgecolor='white', linewidth=1)

for i, (driver, percentage) in enumerate(zip(drivers_list, percentages_actual)):
    ax.text(percentages_diff[i] + 0.1, i, f'{percentage:.1f}%', va='center', fontsize=10, fontweight='bold')

ax.set_xlabel('Distance at Full Throttle (relative difference)', fontsize=12, fontweight='bold')
ax.set_ylabel('Driver', fontsize=12, fontweight='bold')
ax.set_title('Bahrain GP Qualifying - Full Throttle Distance', fontsize=14, fontweight='bold')
ax.invert_yaxis()
ax.grid(axis='x', alpha=0.3, linestyle='--')
ax.set_xlim(left=0)

plt.tight_layout()
plt.show()

Next steps

Analyze throttle application patterns through specific corners
Compare throttle traces between drivers to identify different driving styles
Correlate throttle usage with tire degradation in race stints
Explore the relationship between throttle distance and lap times

​The concept

​Loading the session

​Calculating throttle distance

​Visualizing the results

​Interpreting the results

​Comparing across tracks

​Complete example

​Next steps