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Understanding downforce levels helps reveal car setup philosophy. By comparing average lap speed to maximum speed, we can estimate whether teams are running high-downforce (better cornering) or low-downforce (higher top speeds) configurations. Downforce levels comparison across drivers

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The concept

The downforce metric is calculated as: 
Downforce Level = (Average Speed / Maximum Speed) × 100
  • Higher values indicate more downforce: better cornering speeds but lower top speeds
  • Lower values indicate less downforce: higher top speeds but slower through corners
This is particularly revealing on tracks like Monaco (high downforce) vs Monza (low downforce).

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Loading the session

We’ll analyze qualifying from the 2023 Monaco Grand Prix, a track where downforce is critical. 
import tif1
import matplotlib.pyplot as plt

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

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

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Calculating downforce metrics

For each driver, we calculate their average speed and maximum speed from their fastest lap. 
drivers = session.laps['Driver'].unique()
downforce_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 speeds
        max_speed = telemetry['Speed'].max()
        lap_time = fastest_lap['LapTime'].total_seconds()
        circuit_length = telemetry['Distance'].max()
        avg_speed = (circuit_length / lap_time) * 3.6  # Convert to km/h

        # Downforce metric
        downforce_metric = (avg_speed / max_speed) * 100

        downforce_data.append({
            'Driver': driver,
            'Team': fastest_lap['Team'],
            'Metric': downforce_metric
        })
    except Exception:
        continue

# Sort by metric (highest downforce first)
downforce_data.sort(key=lambda x: x['Metric'], reverse=True)

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Visualizing the results

Create a horizontal bar chart with team colors. To make the differences more apparent, we map the values to the minimum metric, showing relative differences rather than absolute values. 
# Sort by downforce metric
downforce_data.sort(key=lambda x: x['Metric'], reverse=True)

# Map to minimum value to make differences more apparent
min_metric = min(d['Metric'] for d in downforce_data)
for d in downforce_data:
    d['MetricDiff'] = d['Metric'] - min_metric

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

drivers_list = [d['Driver'] for d in downforce_data]
metrics_diff = [d['MetricDiff'] for d in downforce_data]
metrics_actual = [d['Metric'] for d in downforce_data]
colors = [tif1.plotting.get_team_color(d['Team'], session) for d in downforce_data]

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

# Add actual value labels
for i, (driver, metric) in enumerate(zip(drivers_list, metrics_actual)):
    ax.text(metrics_diff[i] + 0.02, i, f'{metric:.2f}',
            va='center', fontsize=10, fontweight='bold')

# Styling
ax.set_xlabel('Downforce Level (relative difference)', fontsize=12, fontweight='bold')
ax.set_ylabel('Driver', fontsize=12, fontweight='bold')
ax.set_title('Monaco GP Qualifying - Downforce Levels',
             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 metric values (labels), making it easier to spot setup variations between drivers.

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Interpreting the results

When analyzing downforce levels, consider:
  • Track characteristics: Monaco requires high downforce; Monza requires low downforce
  • Team philosophy: Some teams prioritize qualifying pace (more downforce) vs race pace (less drag)
  • Weather conditions: Wet conditions often favor higher downforce setups
  • Tire strategy: High downforce can increase tire wear in races

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Comparing teams

You can also aggregate by team to see constructor-level setup choices: 
# Group by team and take the best driver from each
team_data = {}
for d in downforce_data:
    team = d['Team']
    if team not in team_data or d['Metric'] > team_data[team]['Metric']:
        team_data[team] = d

# Sort teams
teams_sorted = sorted(team_data.values(), key=lambda x: x['Metric'], reverse=True)

# Map to minimum for better visualization
min_team_metric = min(t['Metric'] for t in teams_sorted)
for t in teams_sorted:
    t['MetricDiff'] = t['Metric'] - min_team_metric

# Plot team comparison
fig, ax = plt.subplots(figsize=(10, 6))
teams = [t['Team'] for t in teams_sorted]
metrics_diff = [t['MetricDiff'] for t in teams_sorted]
metrics_actual = [t['Metric'] for t in teams_sorted]
colors = [tif1.plotting.get_team_color(t['Team'], session) for t in teams_sorted]

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

# Add actual value labels
for i, metric in enumerate(metrics_actual):
    ax.text(metrics_diff[i] + 0.02, i, f'{metric:.2f}', va='center', fontsize=10, fontweight='bold')

ax.set_xlabel('Downforce Level (relative difference)')
ax.set_title('Team Downforce Comparison')
ax.invert_yaxis()
ax.grid(axis='x', alpha=0.3)
ax.set_xlim(left=0)

plt.tight_layout()
plt.show()

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Complete example

import tif1
import matplotlib.pyplot as plt

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

# Calculate metrics
drivers = session.laps['Driver'].unique()
downforce_data = []

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

        max_speed = telemetry['Speed'].max()
        lap_time = fastest_lap['LapTime'].total_seconds()
        circuit_length = telemetry['Distance'].max()
        avg_speed = (circuit_length / lap_time) * 3.6

        downforce_metric = (avg_speed / max_speed) * 100

        downforce_data.append({
            'Driver': driver,
            'Team': fastest_lap['Team'],
            'Metric': downforce_metric
        })
    except Exception:
        continue

downforce_data.sort(key=lambda x: x['Metric'], reverse=True)

# Map to minimum value to make differences more apparent
min_metric = min(d['Metric'] for d in downforce_data)
for d in downforce_data:
    d['MetricDiff'] = d['Metric'] - min_metric

# Plot
fig, ax = plt.subplots(figsize=(12, 8))
drivers_list = [d['Driver'] for d in downforce_data]
metrics_diff = [d['MetricDiff'] for d in downforce_data]
metrics_actual = [d['Metric'] for d in downforce_data]
colors = [tif1.plotting.get_team_color(d['Team'], session) for d in downforce_data]

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

for i, (driver, metric) in enumerate(zip(drivers_list, metrics_actual)):
    ax.text(metrics_diff[i] + 0.02, i, f'{metric:.2f}', va='center', fontsize=10, fontweight='bold')

ax.set_xlabel('Downforce Level (relative difference)', fontsize=12, fontweight='bold')
ax.set_ylabel('Driver', fontsize=12, fontweight='bold')
ax.set_title('Monaco GP Qualifying - Downforce Levels', 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()

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Next steps

  • Compare downforce levels across different tracks (Monaco vs Monza)
  • Analyze how downforce affects tire degradation in race stints
  • Correlate downforce levels with sector times to identify setup trade-offs
  • Explore the relationship between downforce and weather conditions
Last modified on March 6, 2026