Downforce Level Analysis

Downforce levels reveal car setup philosophy. Compare the average lap speed with the maximum speed. The ratio estimates whether a team runs a high-downforce setup (better cornering) or a low-downforce setup (higher top speeds).

Downforce levels comparison across drivers

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

The contrast is clear on tracks like Monaco (high downforce) and Monza (low downforce).

Loading the Session

Analyze qualifying from the 2023 Monaco Grand Prix, a track where downforce has a large effect.

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')

Calculating Downforce Metrics

For each driver, calculate the average speed and maximum speed from the 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)

Visualizing the Results

Create a horizontal bar chart with team colors. Map the values to the minimum metric to show 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 as bar lengths. The labels show the actual metric values. Together they show setup variations between drivers.

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

Comparing Teams

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()

Parameters

The native function accepts the shared filters. drivers restricts the comparison; the default includes every driver in the session:

import tif1

# All drivers (default)
fig, ax = tif1.plot_downforce_levels(2023, 'Monaco Grand Prix', 'Q')
plt.show()

# Focus on selected drivers
fig, ax = tif1.plot_downforce_levels(2023, 'Monaco Grand Prix', 'Q', drivers=['VER', 'LEC', 'NOR'])
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 qualifying and computes the downforce metric per driver
fig, ax = tif1.plot_downforce_levels(2023, 'Monaco Grand Prix', 'Q')
plt.show()

# Or save straight to a file
# tif1.plot_downforce_levels(2023, 'Monaco Grand Prix', 'Q', save_path="downforce_levels.png")

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

​The Concept

​Loading the Session

​Calculating Downforce Metrics

​Visualizing the Results

​Interpreting the Results

​Comparing Teams

​Parameters

​Complete Example

​Next Steps