Driver Laptimes Distribution Visualization

Visualizae different drivers’ laptime distributions.

import seaborn as sns
from matplotlib import pyplot as plt

import fastf1
import fastf1.plotting


# enabling misc_mpl_mods will turn on minor grid lines that clutters the plot
fastf1.plotting.setup_mpl(mpl_timedelta_support=False, misc_mpl_mods=False)

Load the race session

race = fastf1.get_session(2023, "Azerbaijan", 'R')
race.load()

Get all the laps for the point finishers only. Filter out slow laps (yellow flag, VSC, pitstops etc.) as they distort the graph axis.

point_finishers = race.drivers[:10]
print(point_finishers)
driver_laps = race.laps.pick_drivers(point_finishers).pick_quicklaps()
driver_laps = driver_laps.reset_index()

['11', '1', '16', '14', '55', '44', '18', '63', '4', '22']

To plot the drivers by finishing order, we need to get their three-letter abbreviations in the finishing order.

finishing_order = [race.get_driver(i)["Abbreviation"] for i in point_finishers]
print(finishing_order)

['PER', 'VER', 'LEC', 'ALO', 'SAI', 'HAM', 'STR', 'RUS', 'NOR', 'TSU']

We need to modify the DRIVER_COLORS palette. Its keys are the driver’s full names but we need the keys to be the drivers’ three-letter abbreviations. We can do this with the DRIVER_TRANSLATE mapping.

driver_colors = {abv: fastf1.plotting.DRIVER_COLORS[driver] for abv,
                 driver in fastf1.plotting.DRIVER_TRANSLATE.items()}
print(driver_colors)

{'LEC': '#dc0000', 'SAI': '#ff8181', 'SHW': '#9c0000', 'VER': '#fcd700', 'PER': '#ffec7b', 'DEN': '#907400', 'PIA': '#ff8700', 'NOR': '#eeb370', 'OWA': '#ee6d3a', 'GAS': '#fe86bc', 'OCO': '#ff117c', 'DOO': '#894667', 'BOT': '#00e701', 'ZHO': '#008d01', 'POU': '#004601', 'DEV': '#1e3d61', 'TSU': '#356cac', 'RIC': '#2b4562', 'LAW': '#2b4562', 'HAD': '#1e6176', 'IWA': '#1e6176', 'MAG': '#ffffff', 'HUL': '#cacaca', 'BEA': '#c40000', 'ALO': '#006f62', 'STR': '#00413b', 'DRU': '#2f9b90', 'HAM': '#00d2be', 'RUS': '#24ffff', 'VES': '#00a6ff', 'ALB': '#005aff', 'SAR': '#012564', 'OSU': '#1b3d97'}

First create the violin plots to show the distributions. Then use the swarm plot to show the actual laptimes.

# create the figure
fig, ax = plt.subplots(figsize=(10, 5))

# Seaborn doesn't have proper timedelta support
# so we have to convert timedelta to float (in seconds)
driver_laps["LapTime(s)"] = driver_laps["LapTime"].dt.total_seconds()

sns.violinplot(data=driver_laps,
               x="Driver",
               y="LapTime(s)",
               hue="Driver",
               inner=None,
               density_norm="area",
               order=finishing_order,
               palette=driver_colors
               )

sns.swarmplot(data=driver_laps,
              x="Driver",
              y="LapTime(s)",
              order=finishing_order,
              hue="Compound",
              palette=fastf1.plotting.COMPOUND_COLORS,
              hue_order=["SOFT", "MEDIUM", "HARD"],
              linewidth=0,
              size=4,
              )

<Axes: xlabel='Driver', ylabel='LapTime(s)'>

Make the plot more aesthetic

ax.set_xlabel("Driver")
ax.set_ylabel("Lap Time (s)")
plt.suptitle("2023 Azerbaijan Grand Prix Lap Time Distributions")
sns.despine(left=True, bottom=True)

plt.tight_layout()
plt.show()