Track Temperature Analysis

Track temperature has a large effect on F1 performance. It affects tire grip, degradation, and overall lap times. Visualize how track temperature evolves during a session. This shows the conditions drivers faced and their possible effect on strategy.

Track temperature evolution during a race session

Loading the Session

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')

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)

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()

Understanding the Data

Track temperature patterns show the following:

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

Parameters

The native function accepts the shared filters. With no drivers, it automatically uses the driver with the most recorded laps. This is the logic shown above. Pass a list to draw one line per driver:

import tif1

# Single line for the busiest driver (default)
fig, ax = tif1.plot_track_temperature(2023, 'Monaco', 'R')
plt.show()

# One line per selected driver
fig, ax = tif1.plot_track_temperature(2023, 'Monaco', 'R', drivers=['VER', 'LEC'])
plt.show()

The session’s weather data must be available (it is included in the laps by default).

Complete Example

The whole workflow above is wrapped in a single native function:

import tif1
import matplotlib.pyplot as plt

# One call: loads the race and plots the track temperature across the laps
fig, ax = tif1.plot_track_temperature(2023, 'Monaco', 'R')
plt.show()

# Or save straight to a file
# tif1.plot_track_temperature(2023, 'Monaco', 'R', save_path="track_temperature.png")

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()

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

​Loading the Session

​Getting Weather Data

​Creating the Visualization

​Understanding the Data

​Parameters

​Complete Example

​Adding Air Temperature

​Next Steps