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Track temperature is a critical factor in F1 performance, affecting tire grip, degradation, and overall lap times. By visualizing how track temperature evolves during a session, you can understand the conditions drivers faced and how they might have influenced strategy. Track temperature evolution during a race session

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Loading the session

We’ll analyze track temperature changes during the 2023 Monaco Grand Prix race. 
import tif1
import matplotlib.pyplot as plt

# Load race session
session = tif1.get_session(2023, 'Monaco', 'R')

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Getting weather data

Weather data is automatically included in the laps DataFrame. Each lap has associated weather information like track temperature, air temperature, humidity, and more. 
# Get laps from the driver who completed the most laps
laps = session.laps
driver_counts = laps.groupby('Driver')['LapNumber'].count()
most_laps_driver = driver_counts.idxmax()

# Filter to that driver's laps
driver_laps = laps.pick_drivers(most_laps_driver)

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Creating the visualization

Plot track temperature against lap number to see how conditions evolved. 
fig, ax = plt.subplots(figsize=(12, 6))

# Plot track temperature
ax.plot(driver_laps['LapNumber'], driver_laps['TrackTemp'],
        color='#ff4444', linewidth=2.5, label='Track Temperature')

ax.set_xlabel('Lap Number', fontsize=12)
ax.set_ylabel('Track Temperature (°C)', fontsize=12)
ax.legend(fontsize=11)
ax.grid(alpha=0.3)

plt.suptitle(f"Track Temperature - {session.event['EventName']} {session.event.year}",
             fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

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Understanding the data

Track temperature patterns reveal important insights:
  • Rising temperature: Often seen in afternoon sessions as the sun heats the track surface
  • Cooling trends: Can occur with cloud cover or as the sun sets
  • Stable conditions: Consistent temperature suggests predictable grip levels
  • Temperature spikes: May indicate changing weather or track conditions

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Complete example

import tif1
import matplotlib.pyplot as plt

# Load session
session = tif1.get_session(2023, 'Monaco', 'R')

# Get laps from driver with most laps
laps = session.laps
driver_counts = laps.groupby('Driver')['LapNumber'].count()
most_laps_driver = driver_counts.idxmax()
driver_laps = laps.pick_drivers(most_laps_driver)

# Create plot
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(driver_laps['LapNumber'], driver_laps['TrackTemp'],
        color='#ff4444', linewidth=2.5, label='Track Temperature')

ax.set_xlabel('Lap Number', fontsize=12)
ax.set_ylabel('Track Temperature (°C)', fontsize=12)
ax.legend(fontsize=11)
ax.grid(alpha=0.3)

plt.suptitle(f"Track Temperature - {session.event['EventName']} {session.event.year}",
             fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

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Adding air temperature

Compare track and air temperature to see the relationship between ambient conditions and track surface. 
fig, ax = plt.subplots(figsize=(12, 6))

ax.plot(driver_laps['LapNumber'], driver_laps['TrackTemp'],
        color='#ff4444', linewidth=2.5, label='Track Temperature')
ax.plot(driver_laps['LapNumber'], driver_laps['AirTemp'],
        color='#4444ff', linewidth=2.5, label='Air Temperature')

ax.set_xlabel('Lap Number', fontsize=12)
ax.set_ylabel('Temperature (°C)', fontsize=12)
ax.legend(fontsize=11)
ax.grid(alpha=0.3)

plt.suptitle(f"Temperature Evolution - {session.event['EventName']} {session.event.year}",
             fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

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Next steps

  • Explore other weather data like humidity and wind speed
  • Correlate temperature changes with lap time variations
  • Analyze how different compounds perform at various track temperatures
Last modified on March 6, 2026