Position Changes During a Race

Position changes during a race show race strategy, overtaking opportunities, and driver performance. This tutorial shows how to create a position chart. The chart tracks every driver’s position lap by lap.

Loading the Session

Analyze the 2023 Bahrain Grand Prix, the season opener. This visualization needs lap data only, not telemetry or weather.

import matplotlib.pyplot as plt
import tif1

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

Creating the Visualization

Plot each driver’s position against lap number. Use driver-specific colors to make the chart readable.

# 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(identifier=abb, session=session)

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

Configuring the Plot

Invert the y-axis so position 1 is at the top. Add the appropriate labels.

# 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

Use this visualization to 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, use the native function’s shared filters instead of filtering manually:

import tif1

# Focus on the top 5 finishers
fig, ax = tif1.plot_position_changes(2023, 1, "R", n_drivers=5)
plt.show()

# Or pick specific drivers
fig, ax = tif1.plot_position_changes(2023, 1, "R", drivers=["VER", "LEC", "NOR"])
plt.show()

Axis limits are derived from the data automatically, so the chart works for any grid size without hardcoded y-limits.

Complete Example

The whole workflow above is wrapped in a single native function:

import tif1
import matplotlib.pyplot as plt

# One call: loads the race and plots every driver's position lap by lap
fig, ax = tif1.plot_position_changes(2023, 1, "R")
plt.show()

# Or save straight to a file
# tif1.plot_position_changes(2023, 1, "R", save_path="race_position_changes.png")

Conclusion

Position change charts show the strategic decisions, on-track battles, and performance trends of a race. Combined with tire strategy and pace analysis, they give a complete picture of race dynamics.

​Loading the Session

​Creating the Visualization

​Configuring the Plot

​Interpreting the Results

​Advanced: Highlighting Specific Drivers

​Complete Example

​Conclusion