> ## Documentation Index > Fetch the complete documentation index at: https://tif1.tracinginsights.com/llms.txt > Use this file to discover all available pages before exploring further. # Full Throttle Distance Analysis > Analyze how much of the lap drivers spend at full throttle 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. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} 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%. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} 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. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # 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: ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} 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 ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} 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