Team Pace Comparison#

Rank team’s race pace from the fastest to the slowest.

import seaborn as sns
from matplotlib import pyplot as plt

import fastf1
import fastf1.plotting


# activate the fastf1 color scheme (and no other modifications)
fastf1.plotting.setup_mpl(mpl_timedelta_support=False, misc_mpl_mods=False)

Load the race session. Pick all quick laps (within 107% of fastest lap). For races with mixed conditions, pick_wo_box() is better.

race = fastf1.get_session(2023, "British Grand Prix", 'R')
race.load()
laps = race.laps.pick_quicklaps()

Convert the lap time column from timedelta to integer. This is a seaborn-specific modification. If plotting with matplotlib, set mpl_timedelta_support to true with plotting.setup_mpl.

transformed_laps = laps.copy()
transformed_laps.loc[:, "LapTime (s)"] = laps["LapTime"].dt.total_seconds()

# order the team from the fastest (lowest median lap time) tp slower
team_order = (
    transformed_laps[["Team", "LapTime (s)"]]
    .groupby("Team")
    .median()["LapTime (s)"]
    .sort_values()
    .index
)
print(team_order)

# make a color palette associating team names to hex codes
team_palette = {team: fastf1.plotting.team_color(team) for team in team_order}
Index(['Red Bull Racing', 'McLaren', 'Mercedes', 'Ferrari', 'Aston Martin',
       'AlphaTauri', 'Williams', 'Alpine', 'Alfa Romeo', 'Haas F1 Team'],
      dtype='object', name='Team')
fig, ax = plt.subplots(figsize=(15, 10))
sns.boxplot(
    data=transformed_laps,
    x="Team",
    y="LapTime (s)",
    hue="Team",
    order=team_order,
    palette=team_palette,
    whiskerprops=dict(color="white"),
    boxprops=dict(edgecolor="white"),
    medianprops=dict(color="grey"),
    capprops=dict(color="white"),
)

plt.title("2023 British Grand Prix")
plt.grid(visible=False)

# x-label is redundant
ax.set(xlabel=None)
plt.tight_layout()
plt.show()
2023 British Grand Prix

Total running time of the script: (0 minutes 4.434 seconds)

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