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F1 is often won and lost on tire strategy. Understanding how different compounds perform and when they reach their “cliff” is crucial.

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Getting stint data

tif1 provides the Stint and Compound for every lap. We can use this to map out the entire race strategy. 
import tif1
import pandas as pd

session = tif1.get_session(2025, "British Grand Prix", "Race")
laps = session.laps

# Group by driver and stint to see strategy
strategy = laps.groupby(["Driver", "Stint", "Compound"]).agg({
    "LapNumber": ["min", "max", "count"]
}).reset_index()

print(strategy.head())

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Visualizing the Strategy

A “Gantt-style” chart is the best way to visualize when drivers swapped tires. 
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 8))
drivers = session.drivers[:10] # Top 10 drivers

for i, driver_info in enumerate(drivers):
    driver = driver_info["driver"]
    driver_laps = laps[laps["Driver"] == driver]

    for stint in driver_laps["Stint"].unique():
        stint_laps = driver_laps[driver_laps["Stint"] == stint]
        compound = stint_laps["Compound"].iloc[0]

        # Color mapping
        color = {"SOFT": "red", "MEDIUM": "yellow", "HARD": "white"}.get(compound, "cyan")

        plt.barh(y=driver,
                 width=stint_laps["LapNumber"].max() - stint_laps["LapNumber"].min() + 1,
                 left=stint_laps["LapNumber"].min(),
                 color=color,
                 edgecolor="black")

plt.title("Race Strategy - Top 10 Drivers")
plt.xlabel("Lap Number")
plt.ylabel("Driver")
plt.gca().invert_yaxis()
plt.show()

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Analyzing Degradation

Tire degradation is the increase in lap time as the tires wear out. We can calculate this by looking at the slope of lap times during a long stint. 
# Filter for a long stint (e.g., VER Stint 2)
ver_stint_2 = laps[(laps["Driver"] == "VER") & (laps["Stint"] == 2)]

# Simple linear regression to find degradation rate
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(
    ver_stint_2["TyreLife"],
    ver_stint_2["LapTime"]
)

print(f"Degradation Rate: {slope:.4f} seconds per lap")

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What the slope tells us

  • Slope > 0: The driver is losing time as the tire wears (typical).
  • Slope < 0: The driver is actually getting faster (often due to fuel burn-off overcoming tire wear).
  • Near Zero: Excellent tire management.

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Comparing Compounds

We can also compare the performance delta between Soft, Medium, and Hard tires. 
# Average lap time per compound (cleaned)
clean_laps = laps[laps["LapTime"] < laps["LapTime"].min() * 1.1]
compound_performance = clean_laps.groupby("Compound")["LapTime"].mean()

print(compound_performance)

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Summary

With tif1, you have access to detailed tire information that allows you to reconstruct race strategies and quantify the performance characteristics of every compound used in the race.