---
title: "Driver Lap Times Visualization"
icon: "chart-scatter"
description: "Visualize driver lap times with compound color coding"
keywords:
  - "lap times"
  - "scatter plot"
  - "tire compounds"
  - "driver analysis"
  - "race visualization"
---

This tutorial shows how to create a scatter plot of a driver's lap times throughout a race, with color coding based on tire compounds used. This visualization helps identify tire performance, consistency, and strategy effectiveness.

<img
  src="/assets/driver_laptimes_example.png"
  alt="Driver lap times scatter plot showing Alonso's lap times in the 2023 Azerbaijan Grand Prix"
/>

## Setup

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

# Enable Matplotlib patches for plotting timedelta values and load
# tif1's dark color scheme
tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')
```

## Load the Race Session

```python
# Load the race session
race = tif1.get_session(2023, "Azerbaijan", 'R')
laps = race.laps
```

## Get Driver Laps

Filter laps for a specific driver and remove slow laps that would distort the visualization.

```python
# Get all laps for a single driver
# Filter out slow laps as they distort the graph axis
driver_laps = laps[laps["Driver"] == "ALO"].copy()

# Remove slow laps (pit laps, safety car, etc.)
# Keep only laps within 107% of the driver's fastest lap
fastest_lap = driver_laps["LapTime"].min()
driver_laps = driver_laps[driver_laps["LapTime"] < fastest_lap * 1.07]

# Remove deleted/invalid laps
driver_laps = driver_laps[~driver_laps["Deleted"]]

# Reset index for cleaner plotting
driver_laps = driver_laps.reset_index(drop=True)
```

## Create the Scatter Plot

Make a scatter plot using lap number as the x-axis and lap time as the y-axis. Marker colors correspond to the tire compounds used.

```python
fig, ax = plt.subplots(figsize=(10, 8))

# Create scatter plot with compound-based coloring
sns.scatterplot(
    data=driver_laps,
    x="LapNumber",
    y="LapTime",
    ax=ax,
    hue="Compound",
    palette=tif1.plotting.get_compound_mapping(session=race),
    s=80,
    linewidth=0,
    legend='auto'
)
```

## Enhance the Plot

Make the plot more aesthetic and easier to read.

```python
# Set axis labels
ax.set_xlabel("Lap Number", fontsize=12)
ax.set_ylabel("Lap Time", fontsize=12)

# The y-axis increases from bottom to top by default
# Since we are plotting time, it makes sense to invert the axis
ax.invert_yaxis()

# Add title
plt.suptitle("Alonso Lap Times in the 2023 Azerbaijan Grand Prix",
             fontsize=14, fontweight='bold')

# Turn on major grid lines
plt.grid(color='w', which='major', axis='both', alpha=0.3)

# Remove spines for cleaner look
sns.despine(left=True, bottom=True)

plt.tight_layout()
plt.show()
```

## Complete Example

Here's the full code in one block:

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

# Setup plotting
tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# Load race session
race = tif1.get_session(2023, "Azerbaijan", 'R')
laps = race.laps

# Get driver laps and filter
driver_laps = laps[laps["Driver"] == "ALO"].copy()
fastest_lap = driver_laps["LapTime"].min()
driver_laps = driver_laps[driver_laps["LapTime"] < fastest_lap * 1.07]
driver_laps = driver_laps[~driver_laps["Deleted"]].reset_index(drop=True)

# Create scatter plot
fig, ax = plt.subplots(figsize=(10, 8))

sns.scatterplot(
    data=driver_laps,
    x="LapNumber",
    y="LapTime",
    ax=ax,
    hue="Compound",
    palette=tif1.plotting.get_compound_mapping(session=race),
    s=80,
    linewidth=0,
    legend='auto'
)

# Enhance plot
ax.set_xlabel("Lap Number", fontsize=12)
ax.set_ylabel("Lap Time", fontsize=12)
ax.invert_yaxis()
plt.suptitle("Alonso Lap Times in the 2023 Azerbaijan Grand Prix",
             fontsize=14, fontweight='bold')
plt.grid(color='w', which='major', axis='both', alpha=0.3)
sns.despine(left=True, bottom=True)

plt.tight_layout()
plt.show()
```

## Analyzing Multiple Drivers

You can extend this to compare multiple drivers side by side:

```python
# Compare top 3 finishers
final_lap = laps[laps["LapNumber"] == laps["LapNumber"].max()]
top_3_drivers = final_lap.sort_values("Position").head(3)["Driver"].tolist()

fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True)

for idx, driver in enumerate(top_3_drivers):
    ax = axes[idx]

    # Filter driver laps
    driver_laps = laps[laps["Driver"] == driver].copy()
    fastest_lap = driver_laps["LapTime"].min()
    driver_laps = driver_laps[driver_laps["LapTime"] < fastest_lap * 1.07]
    driver_laps = driver_laps[~driver_laps["Deleted"]]

    # Plot
    sns.scatterplot(
        data=driver_laps,
        x="LapNumber",
        y="LapTime",
        ax=ax,
        hue="Compound",
        palette=tif1.plotting.get_compound_mapping(session=race),
        s=60,
        linewidth=0,
        legend=(idx == 2)  # Only show legend on last plot
    )

    ax.set_xlabel("Lap Number")
    ax.set_ylabel("Lap Time" if idx == 0 else "")
    ax.set_title(f"{driver}", fontweight='bold')
    ax.invert_yaxis()
    ax.grid(color='w', which='major', axis='both', alpha=0.3)
    sns.despine(left=True, bottom=True, ax=ax)

plt.suptitle("Top 3 Finishers - Lap Time Comparison",
             fontsize=16, fontweight='bold', y=1.02)
plt.tight_layout()
plt.show()
```

## Insights from the Visualization

This type of plot reveals several key insights:

1. **Tire Performance**: Different compounds show distinct lap time clusters
2. **Degradation**: Upward trends within a compound indicate tire wear
3. **Consistency**: Tight clustering shows consistent driving
4. **Strategy**: Compound changes and their timing are clearly visible
5. **Outliers**: Slow laps (traffic, mistakes) stand out immediately

## Customization Options

You can customize the plot further:

```python
# Use custom colors
custom_palette = {
    "SOFT": "#FF0000",
    "MEDIUM": "#FFA500",
    "HARD": "#FFFFFF",
    "INTERMEDIATE": "#00FF00",
    "WET": "#0000FF"
}

# Adjust marker size and style
sns.scatterplot(
    data=driver_laps,
    x="LapNumber",
    y="LapTime",
    hue="Compound",
    palette=custom_palette,
    s=100,  # Larger markers
    marker='D',  # Diamond markers
    alpha=0.7,  # Transparency
    edgecolor='black',  # Marker outline
    linewidth=0.5
)
```

## Summary

This tutorial demonstrated how to create an effective lap time visualization that combines temporal data (lap number) with performance data (lap time) and strategic information (tire compound). The scatter plot format makes it easy to spot patterns, outliers, and strategic decisions throughout a race.

---

## Related Pages

<CardGroup cols={2}>
  <Card title="Tire Strategy" href="/tutorials/tire-strategy">
    Detailed tire analysis
  </Card>
  <Card title="Race Pace Analysis" href="/tutorials/race-pace-analysis">
    Compare race pace
  </Card>
  <Card title="Plotting API" href="/api-reference/plotting">
    Plotting reference
  </Card>
  <Card title="Complete Race Analysis" href="/tutorials/race-analysis">
    Full race workflow
  </Card>
</CardGroup>
