Season Summary Visualization

This example demonstrates how to make an interactive season summarization dashboard showing points scored by each driver at each round.

import pandas as pd
import plotly.graph_objects as go
from plotly.io import show
from plotly.subplots import make_subplots

import fastf1 as ff1

season = 2024
schedule = ff1.get_event_schedule(season, include_testing=False)

Get each driver’s finishing positions and points total for each round.

standings = []
# Shorten the event names by trimming Grand Prix from the name.
# This will be used to label our graph.
short_event_names = []

for _, event in schedule.iterrows():
    event_name, round_number = event["EventName"], event["RoundNumber"]
    short_event_names.append(event_name.replace("Grand Prix", "").strip())

    # Only need to load the results data
    race = ff1.get_session(season, event_name, "R")
    race.load(laps=False, telemetry=False, weather=False, messages=False)

    # Add sprint race points if applicable
    sprint = None
    # F1 has used different names for the sprint race event format
    # From 2024 onwards, it has been "sprint_qualifying"
    # In 2023, you should match on "sprint_shootout"
    # In 2022 and 2021, you should match on "sprint"
    if event["EventFormat"] == "sprint_qualifying":
        sprint = ff1.get_session(season, event_name, "S")
        sprint.load(laps=False, telemetry=False, weather=False, messages=False)

    for _, driver_row in race.results.iterrows():
        abbreviation, race_points, race_position = (
            driver_row["Abbreviation"],
            driver_row["Points"],
            driver_row["Position"],
        )

        sprint_points = 0
        if sprint is not None:
            driver_row = sprint.results[
                sprint.results["Abbreviation"] == abbreviation
            ]
            if not driver_row.empty:
                # We need the values[0] accessor because driver_row is actually
                # returned as a dataframe with a single row
                sprint_points = driver_row["Points"].values[0]

        standings.append(
            {
                "EventName": event_name,
                "RoundNumber": round_number,
                "Driver": abbreviation,
                "Points": race_points + sprint_points,
                "Position": race_position,
            }
        )

Now we have a dataframe where each row can be seen as two parts. [“EventName”, “RoundNumber”, “Driver”] which act like an index. [“Points”, “Position”] which contain the actual data we want to plot.

df = pd.DataFrame(standings)

We remake it into an easier to use format where the row indices are the drivers, and the columns are the races. This allows us to look up the points scored by a driver at a race more easily.

heatmap_data = df.pivot(
    index="Driver", columns="RoundNumber", values="Points"
).fillna(0)

# Save the final drivers standing and sort the data such that the lowest-
# scoring driver is towards the bottom
heatmap_data["total_points"] = heatmap_data.sum(axis=1)
heatmap_data = heatmap_data.sort_values(by="total_points", ascending=True)
total_points = heatmap_data["total_points"].values
heatmap_data = heatmap_data.drop(columns=["total_points"])

# Do the same for position.
position_data = df.pivot(
    index="Driver", columns="RoundNumber", values="Position"
).fillna("N/A")

Prepare the hover text

hover_info = [
    [
        {
            "position": position_data.at[driver, race],
        }
        for race in schedule["RoundNumber"]
    ]
    for driver in heatmap_data.index
]

Create the subplots for the two heatmaps

fig = make_subplots(
    rows=1,
    cols=2,
    column_widths=[0.85, 0.15],
    subplot_titles=("F1 2024 Season Summary", "Total Points"),
)
fig.update_layout(width=900, height=800)

# Per round summary heatmap
fig.add_trace(
    go.Heatmap(
        # Use the race names as x labels and the driver abbreviations
        # as the y labels
        x=short_event_names,
        y=heatmap_data.index,
        z=heatmap_data.values,
        # Use the points scored as overlay text
        text=heatmap_data.values,
        texttemplate="%{text}",
        textfont={"size": 12},
        customdata=hover_info,
        hovertemplate=(
            "Driver: %{y}<br>"
            "Race Name: %{x}<br>"
            "Points: %{z}<br>"
            "Position: %{customdata.position}<extra></extra>"
        ),
        colorscale="YlGnBu",
        showscale=False,
        zmin=0,
        # We need to set zmax for the two heatmaps separately as the
        # max value in the total points plot is significantly higher.
        zmax=heatmap_data.values.max(),
    ),
    row=1,
    col=1,
)

# Heatmap for total points
fig.add_trace(
    go.Heatmap(
        x=["Total Points"] * len(total_points),
        y=heatmap_data.index,
        z=total_points,
        text=total_points,
        texttemplate="%{text}",
        textfont={"size": 12},
        colorscale="YlGnBu",
        showscale=False,
        zmin=0,
        zmax=total_points.max(),
    ),
    row=1,
    col=2,
)

# Plot the updated heatmap
show(fig)