> ## 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.

# Race Pace Analysis

> Learn how to compare driver performance and consistency

Race pace analysis is one of the most common tasks in F1 data science. It involves looking at lap times over a long stint to see which driver is faster and how well they manage their tires.

<img src="https://mintcdn.com/tracinginsightscom/UgQgVDzHEEleARPO/assets/race_pace_boxplot.png?fit=max&auto=format&n=UgQgVDzHEEleARPO&q=85&s=85c68264f2b6d2dd6b5a37b90f4d0ac9" alt="Race pace comparison box plot" width="1784" height="881" data-path="assets/race_pace_boxplot.png" />

## Loading the Data

For this analysis, we'll look at the 2024 Abu Dhabi Grand Prix. We'll use the `polars` lib for faster filtering.

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

We need to filter these out to get a "true" race pace.

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

Let's compare the pace of the top two finishers.

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

To see how the pace evolved (tire degradation), we can plot lap times against lap numbers.

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

By following this workflow, you can:

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