Race Pace Analysis

Race pace analysis is a common task in F1 data science. The analysis examines lap times over a long stint. The results show which driver is faster and how well each driver manages the tires.

Loading the Data

This example uses the 2024 Abu Dhabi Grand Prix. Use the polars lib for faster filtering.

import tif1
import matplotlib.pyplot as plt
import seaborn as sns

# Load the session with polars lib
session = tif1.get_session(2024, "Abu Dhabi Grand Prix", "Race", lib="polars")
laps = session.laps

Cleaning the Data

In a race, lap times can be skewed by:

Safety Car periods (very slow laps)
Pit stops (very slow laps)
Lap 1 (standing start)

Filter these laps out to get a “true” race pace.

# Convert to pandas for easier plotting
df = laps.to_pandas()

# Filter out very slow laps and pit laps
# Typically, race laps are within a certain range
clean_laps = df[
    (df["LapTime"] < df["LapTime"].min() * 1.07) & # Within 7% of fastest lap
    (df["PitInTime"].isna()) &
    (df["PitOutTime"].isna()) &
    (df["LapNumber"] > 1)
]

Comparing Drivers

Compare the pace of the top two finishers.

# Filter for specific drivers
drivers = ["VER", "LEC"]
comparison_df = clean_laps[clean_laps["Driver"].isin(drivers)]

# Create a boxplot to see distribution of lap times
plt.figure(figsize=(10, 6))
sns.boxplot(x="Driver", y="LapTime", data=comparison_df, palette="viridis")
plt.title("Race Pace Comparison: VER vs LEC")
plt.ylabel("Lap Time (s)")
plt.show()

Interpreting the Results

Median (line in the box): Represents the typical race pace.
Box size (IQR): Represents consistency. A smaller box means the driver was more consistent.
Whiskers/Outliers: Show variance caused by traffic or minor errors.

Visualizing Pace Over the Race

Plot lap times against lap numbers to see how the pace evolved (tire degradation).

plt.figure(figsize=(12, 6))
for driver in drivers:
    driver_laps = comparison_df[comparison_df["Driver"] == driver]
    plt.plot(driver_laps["LapNumber"], driver_laps["LapTime"], label=driver)

plt.title("Lap Times Evolution")
plt.xlabel("Lap Number")
plt.ylabel("Lap Time (s)")
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Conclusion

Follow this workflow to:

Load high-quality timing data.
Clean it to remove anomalies.
Compare drivers using statistical distributions.
Visualize performance trends across the entire race.

​Loading the Data

​Cleaning the Data

​Comparing Drivers

​Interpreting the Results

​Visualizing Pace Over the Race

​Conclusion