> ## 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.

# Position Changes During a Race

> Visualize how driver positions evolve throughout a race

Understanding how positions change during a race provides insights into race strategy, overtaking opportunities, and driver performance. This tutorial shows you how to create a position chart that tracks every driver's position lap by lap.

<img src="https://mintcdn.com/tracinginsightscom/UgQgVDzHEEleARPO/assets/race_position_changes.png?fit=max&auto=format&n=UgQgVDzHEEleARPO&q=85&s=d1b10d6f8892e5c999892ac177db2bb0" alt="Race position changes visualization" width="2391" height="1483" data-path="assets/race_position_changes.png" />

## Loading the Session

We'll analyze the 2023 Bahrain Grand Prix, the season opener. For this visualization, we only need lap data, not telemetry or weather.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import matplotlib.pyplot as plt
import tif1

# Load the race session
session = tif1.get_session(2023, 1, "R")
laps = session.laps
```

## Creating the Visualization

The key is to plot each driver's position against lap number. We'll use driver-specific colors to make the chart readable.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Setup plotting with F1 theme
tif1.plotting.setup_mpl(mpl_timedelta_support=False, color_scheme="fastf1")

# Create the figure
fig, ax = plt.subplots(figsize=(8.0, 4.9))

# Plot each driver's position over the race
for drv in laps["Driver"].unique():
    drv_laps = laps[laps["Driver"] == drv]

    # Get driver abbreviation and color
    abb = drv_laps["Driver"].iloc[0]
    color = tif1.plotting.get_driver_color(driver=abb, session=session)

    # Plot position vs lap number
    ax.plot(drv_laps["LapNumber"], drv_laps["Position"], label=abb, color=color)
```

## Configuring the Plot

To make the chart intuitive, we invert the y-axis so position 1 is at the top, and add appropriate labels.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Configure axes
ax.set_ylim([20.5, 0.5])  # Invert so P1 is at top
ax.set_yticks([1, 5, 10, 15, 20])
ax.set_xlabel("Lap")
ax.set_ylabel("Position")

# Add legend outside plot area to avoid clutter
ax.legend(bbox_to_anchor=(1.0, 1.02))
plt.tight_layout()

plt.show()
```

## Interpreting the Results

From this visualization, you can identify:

* **Starting grid**: Where each driver began the race
* **Overtakes**: Sharp position changes indicate on-track passes
* **Pit stop windows**: Drivers drop positions during pit stops, then recover
* **Race incidents**: Sudden position losses may indicate crashes or mechanical issues
* **Consistent performers**: Flat lines show drivers maintaining position
* **Strategic gains**: Gradual position improvements often indicate better strategy

## Advanced: Highlighting Specific Drivers

To focus on specific drivers, you can filter the data before plotting:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Focus on top 5 finishers
final_lap = laps[laps["LapNumber"] == laps["LapNumber"].max()]
top_5 = final_lap.sort_values("Position").head(5)["Driver"].tolist()

fig, ax = plt.subplots(figsize=(10, 6))

for drv in top_5:
    drv_laps = laps[laps["Driver"] == drv]
    abb = drv_laps["Driver"].iloc[0]
    color = tif1.plotting.get_driver_color(driver=abb, session=session)

    ax.plot(
        drv_laps["LapNumber"],
        drv_laps["Position"],
        label=abb,
        color=color,
        linewidth=2.5,
        marker="o",
        markersize=3
    )

ax.set_ylim([20.5, 0.5])
ax.set_xlabel("Lap Number")
ax.set_ylabel("Position")
ax.set_title("Top 5 Finishers - Position Changes")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()
```

## Conclusion

Position change charts are essential for race analysis. They reveal the story of the race at a glance, showing strategic decisions, on-track battles, and performance trends. Combined with tire strategy and pace analysis, they provide a complete picture of race dynamics.