> ## 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.

# Tire Strategy Analysis

> Analyzing tire compounds, degradation, and stint lengths

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

<img src="https://mintcdn.com/tracinginsightscom/UgQgVDzHEEleARPO/assets/race_tire_strategy.png?fit=max&auto=format&n=UgQgVDzHEEleARPO&q=85&s=33273da64a319c4d551155d4749408cf" alt="Tire strategy Gantt chart showing compound usage" width="2383" height="1781" data-path="assets/race_tire_strategy.png" />

## Getting stint data

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

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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())
```

## Visualizing the Strategy

A "Gantt-style" chart is the best way to visualize when drivers swapped tires.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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()
```

## 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.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# 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")
```

### 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.

## Comparing Compounds

We can also compare the performance delta between Soft, Medium, and Hard tires.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# 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)
```

## 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.