Championship Standings Examples

Track championship progress and analyze season-long performance

These examples demonstrate how to analyze championship standings, season-long performance, and track driver/team positions throughout the season.

Results Tracker

Create a heatmap showing points scored by each driver at each race.

import pandas as pd
import plotly.express as px
from plotly.io import show
from fastf1.ergast import Ergast

# Load results for season 2022
ergast = Ergast()
races = ergast.get_race_schedule(2022)
results = []

# For each race in the season
for rnd, race in races['raceName'].items():
    # Get results (round no. starts from 1)
    temp = ergast.get_race_results(season=2022, round=rnd + 1)
    temp = temp.content[0]
    
    # If there is a sprint, get the results as well
    sprint = ergast.get_sprint_results(season=2022, round=rnd + 1)
    if sprint.content and sprint.description['round'][0] == rnd + 1:
        temp = pd.merge(temp, sprint.content[0], on='driverCode', how='left')
        # Add sprint points and race points to get the total
        temp['points'] = temp['points_x'] + temp['points_y']
        temp.drop(columns=['points_x', 'points_y'], inplace=True)
    
    # Add round no. and grand prix name
    temp['round'] = rnd + 1
    temp['race'] = race.removesuffix(' Grand Prix')
    temp = temp[['round', 'race', 'driverCode', 'points']]
    results.append(temp)

# Append all races into a single dataframe
results = pd.concat(results)
races = results['race'].drop_duplicates()

# Reshape to wide table (drivers as rows, races as columns)
results = results.pivot(index='driverCode', columns='round', values='points')

# Rank drivers by total points
results['total_points'] = results.sum(axis=1)
results = results.sort_values(by='total_points', ascending=False)
results.drop(columns='total_points', inplace=True)

# Use race names as column names
results.columns = races

# Plot heatmap using plotly
fig = px.imshow(
    results,
    text_auto=True,
    aspect='auto',
    color_continuous_scale=[[0,    'rgb(198, 219, 239)'],
                            [0.25, 'rgb(107, 174, 214)'],
                            [0.5,  'rgb(33,  113, 181)'],
                            [0.75, 'rgb(8,   81,  156)'],
                            [1,    'rgb(8,   48,  107)']],
    labels={'x': 'Race',
            'y': 'Driver',
            'color': 'Points'}
)
fig.update_xaxes(title_text='')
fig.update_yaxes(title_text='')
fig.update_yaxes(tickmode='linear')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey',
                 showline=False,
                 tickson='boundaries')
fig.update_xaxes(showgrid=False, showline=False)
fig.update_layout(plot_bgcolor='rgba(0,0,0,0)')
fig.update_layout(coloraxis_showscale=False)
fig.update_layout(xaxis=dict(side='top'))
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
show(fig)

What This Example Shows

• Using the Ergast API for historical data
• Combining race and sprint results
• Reshaping data with pandas pivot tables
• Creating interactive heatmaps with Plotly
• Handling sprint race weekends

Expected Output

An interactive heatmap where:

• Rows: Drivers (ordered by championship position)
• Columns: Races (in chronological order)
• Cell color: Darker blue = more points
• Cell value: Points scored at that race
• Hover: Shows driver, race, and points

Understanding the Data

• DNF/DNS: Shows as 0 points
• Sprint weekends: Points combined from sprint and race
• Driver ordering: Top of chart = championship leader

Season Summary

Create an interactive dashboard showing season performance with total points.

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 for each round
standings = []
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())
    
    # Load race results
    race = ff1.get_session(season, event_name, "R")
    race.load(laps=False, telemetry=False, weather=False, messages=False)
    
    # Check for sprint race
    sprint = None
    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:
                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,
            }
        )

# Create dataframe
df = pd.DataFrame(standings)

# Reshape to heatmap format
heatmap_data = df.pivot(
    index="Driver", columns="RoundNumber", values="Points"
).fillna(0)

# Sort by total points
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 hover info
hover_info = [
    [
        {
            "position": position_data.at[driver, race],
        }
        for race in schedule["RoundNumber"]
    ]
    for driver in heatmap_data.index
]

# Create subplots for 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(
        x=short_event_names,
        y=heatmap_data.index,
        z=heatmap_data.values,
        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,
        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,
)

show(fig)

What This Example Shows

• Loading full season data efficiently
• Creating multi-panel Plotly dashboards
• Handling different sprint race formats by year
• Adding custom hover information
• Combining per-race and total points views

Expected Output

An interactive dashboard with two heatmaps:

1. Left panel: Points scored at each race
2. Right panel: Total championship points

Features:

• Hover shows driver, race, points, and position
• Color intensity indicates points scored
• Drivers ordered by total points (bottom to top)

Sprint Race Formats by Year

# 2024 onwards
if event["EventFormat"] == "sprint_qualifying":

# 2023
if event["EventFormat"] == "sprint_shootout":

# 2021-2022
if event["EventFormat"] == "sprint":

Championship Calculator

Calculate which drivers can still mathematically win the championship.

import fastf1
from fastf1.ergast import Ergast

SEASON = 2025
ROUND = 12

# Get current driver standings
def get_drivers_standings():
    ergast = Ergast()
    standings = ergast.get_driver_standings(season=SEASON, round=ROUND)
    return standings.content[0]

# Calculate maximum points for remaining season
def calculate_max_points_for_remaining_season():
    POINTS_FOR_SPRINT = 8 + 25  # Winning sprint and race
    POINTS_FOR_CONVENTIONAL = 25  # Winning race
    
    events = fastf1.events.get_event_schedule(SEASON, backend='ergast')
    events = events[events['RoundNumber'] > ROUND]
    
    # Count sprint and conventional races
    sprint_events = len(events.loc[events["EventFormat"] == "sprint_shootout"])
    conventional_events = len(events.loc[events["EventFormat"] == "conventional"])
    
    # Calculate points for each
    sprint_points = sprint_events * POINTS_FOR_SPRINT
    conventional_points = conventional_events * POINTS_FOR_CONVENTIONAL
    
    return sprint_points + conventional_points

# Determine who can win
def calculate_who_can_win(driver_standings, max_points):
    LEADER_POINTS = int(driver_standings.loc[0]['points'])
    
    for i, _ in enumerate(driver_standings.iterrows()):
        driver = driver_standings.loc[i]
        driver_max_points = int(driver["points"]) + max_points
        can_win = 'No' if driver_max_points < LEADER_POINTS else 'Yes'
        
        print(f"{driver['position']}: {driver['givenName'] + ' ' + driver['familyName']}, "
              f"Current points: {driver['points']}, "
              f"Theoretical max points: {driver_max_points}, "
              f"Can win: {can_win}")

# Execute calculation
driver_standings = get_drivers_standings()
points = calculate_max_points_for_remaining_season()
calculate_who_can_win(driver_standings, points)

What This Example Shows

• Using Ergast API for current standings
• Calculating remaining points available
• Accounting for sprint vs conventional races
• Mathematical championship possibility analysis

Expected Output

A text output listing each driver with:

• Current championship position
• Current points total
• Theoretical maximum points (if they win everything)
• Whether they can mathematically win

Example Output

1: Max Verstappen, Current points: 350, Theoretical max points: 525, Can win: Yes
2: Sergio Perez, Current points: 280, Theoretical max points: 455, Can win: Yes
3: Lewis Hamilton, Current points: 200, Theoretical max points: 375, Can win: Yes
4: Charles Leclerc, Current points: 180, Theoretical max points: 355, Can win: Yes
5: Carlos Sainz, Current points: 150, Theoretical max points: 325, Can win: No
...

Points System

Current F1 points:

• Race: 25, 18, 15, 12, 10, 8, 6, 4, 2, 1 (top 10)
• Sprint: 8, 7, 6, 5, 4, 3, 2, 1 (top 8)
• Fastest lap: +1 (if finishing in top 10)

Working with Ergast API

The Ergast API provides historical F1 data:

Available Methods

from fastf1.ergast import Ergast

ergast = Ergast()

# Race schedule
schedule = ergast.get_race_schedule(season=2023)

# Race results
results = ergast.get_race_results(season=2023, round=5)

# Sprint results
sprint = ergast.get_sprint_results(season=2023, round=5)

# Driver standings
standings = ergast.get_driver_standings(season=2023, round=10)

# Constructor standings
team_standings = ergast.get_constructor_standings(season=2023, round=10)

# Qualifying results
quali = ergast.get_qualifying_results(season=2023, round=5)

Ergast vs FastF1

Use Ergast when:

• You need historical championship data
• You want quick access to results without loading full sessions
• You’re doing multi-season analysis

Use FastF1 when:

• You need telemetry data
• You want lap-by-lap details
• You need timing data
• You’re analyzing recent races (2018+)

Performance Tips

Loading Season Data

# Only load what you need
race.load(laps=False, telemetry=False, weather=False, messages=False)

# For standings analysis, you only need results
race.load(laps=False, telemetry=False, weather=False, messages=False)

Caching

# FastF1 automatically caches data
# First run: slow (downloads data)
# Subsequent runs: fast (uses cache)

# Clear cache if needed
import fastf1
fastf1.Cache.clear_cache()

The Ergast API uses historical data and may have a delay of a few days after each race. For the most recent race, use FastF1’s session loading instead.