As of 2025, Lewis Hamilton and Michael Schumacher had the joint-most World Championship titles, with ***** each. Meanwhile, the constructor with the most World Championship titles was Ferrari.

As of 2025, Lewis Hamilton had won more World Championship points than any other driver, with a total of over 4,000. Meanwhile, Max Verstappen and Sebastian Vettel ranked in second and third place respectively.

Creation 🏎️

This dataset was created by web scraping public sources(Official F1 page, wikipedia and F1 fandom web pages), capturing key details of each Formula 1 race, such as date, circuit, winner, team, time, and laps completed by the winner in the grand prix.

Use case 🏁

This dataset can be used to analyze the performance of drivers by comparing how their results evolve throughout different seasons. It also allows:

• Evaluating team performance to identify which constructors dominate specific circuits or periods of time.

• Historical comparisons can be made to contrast race times and victories across different eras.

• The study of driver–circuit affinity, highlighting which drivers consistently excel at certain tracks .

History 🌍

Formula 1, held globally since 1950, is one of the most prestigious motorsport competitions. Over the decades, legends like Michael Schumacher and Lewis Hamilton have dominated the championship. The all-time race win record is shared by Hamilton and Schumacher. Iconic circuits such as Monza, Monaco, and Silverstone have shaped the sport’s legacy, while teams like Ferrari, McLaren, and Mercedes stand as historic powerhouses.

Inspiration⭐

I wanted to create this dataset to get started on Kaggle and to broadly understand F1 winners, with the goal of creating a more robust dataset to predict and uncover traits in F1 winners.

Context

Formula One is the highest class of international racing for open-wheel single-seater racing cars sanctioned by the Fédération Internationale de l'Automobile (FIA). Ever since its inaugural season in 1950, Formula1 has been regarded as the pinnacle of motorsport.

Content

This dataset contains detailed information about qualifying and race results for all the tracks over the course of multiple seasons. There is a separate directory for each season. There are 2 sub-directories for each season, namely: Qualifying Results and Race Results. The Race Results directory contains an overall_race_results.csv file which summarizes the race results throughout the entire season. It also contains multiple .csv files for the results of each race in the season. The Qualifying Results directory contains multiple .csv files for the qualifying results before the start of each race.

Note

For the 1982 season and before the qualifying results contain only 1 entry in the file which is that of the polesitter. The lap times of the other drivers were not accounted for, and on the official website there is only 1 entry under the qualifying results.

Inspiration

F1 is one of my favorite sports and I almost never miss a race 😄

The motivation behind creating this dataset was to learn more about web scraping and try to perform a statistical analysis of the data. Some of the things you could do with the entire dataset are as follows: - Identify the driver with the most poles - Compare qualifying times of different drivers (championship contenders, team-mates, etc) - Determine how often a particular driver out-qualifies his team-mate - Compare qualifying lap times of a race from previous seasons - Identify the driver with the most number of wins at a particular track - Analyze how the championship battle unfolded based on the number of points scored by the drivers (specially interesting for the 2021 f1 season 👀) - Identify drivers with the highest number of wins, podiums, DNFs, etc - Compare the average lap times of different tracks to identify the slowest and fastest tracks on the calendar - Compare the number of laps for each race in the season (Belgium 2021 being the clear winner 😂) - Find out who won the Driver's Championship based on the total number of points - Find out who won the Constructor's Championship based on the total number of points for each team

Some Common F1 Terms You Might Come Across

• DNF: Did Not Finish. Commonly used nomenclature for drivers that crashed/failed to complete the entire race
• DNQ: Did Not Qualify. Eliminated missing values from the qualifying datasets by introducing this abbreviation for drivers who failed to qualify.
• NC: Not Confirmed. For drivers that DNF the term NC is used in the Position column
• DQ: Disqualified. Generally drivers are disqualified from races due to technical infringements or a breach of sporting regulations (Example: Sebastian Vettel was disqualified from the 2021 Hungarian Grand Prix due to fuel irregularites and stripped of all the points he earned from finishing the race in P2)

Future Work

As I collect more data for the previous seasons, I will create new versions for the dataset. The goal with this dataset is to create an archive of qualifying and race data from 1950-2021. The dataset will also be updated when the 2022 season commences.

Formula 1 Comprehensive Dataset (2020-2025)

Dataset Description This comprehensive Formula 1 dataset contains detailed racing data spanning from 2020 to 2025, including race results, qualifying sessions, championship standings, circuit information, and historical driver statistics.

Perfect for:

📊 F1 performance analysis

🤖 Machine learning projects

📈 Data visualization

🏆 Championship predictions

📋 Racing statistics research

📁 Files Included 1. f1_race_results_2020_2025.csv (53 entries) Race winners and results from Grand Prix weekends

Date, Grand Prix name, race winner

Constructor, nationality, grid position

Race time, fastest lap time, points scored

1. f1_qualifying_results_2020_2024.csv (820 entries) Qualifying session results with timing data

Q1, Q2, Q3 session times

Grid positions, laps completed

Driver and constructor information

1. f1_driver_standings_progressive.csv (600 entries) Championship standings progression throughout seasons

Points accumulation over race weekends

Wins, podiums, pole positions tracking

Season-long championship battle data

1. f1_constructor_standings_progressive.csv (360 entries) Team championship standings evolution

Constructor points and wins

Team performance metrics

Manufacturer rivalry data

1. f1_circuits_technical_data.csv (24 entries) Technical specifications for all F1 circuits

Track length, number of turns

Lap records and record holders

Circuit designers and first F1 usage

1. f1_historical_driver_statistics.csv (30 entries) All-time career statistics for F1 drivers

Career wins, poles, podiums

Racing entries and achievements

Active and retired driver records

1. f1_comprehensive_dataset_2020_2025.csv (432 entries) MAIN DATASET - Combined data from all sources

Multiple data types in one file

Ready for immediate analysis

Comprehensive F1 information hub

🔧 Data Features Clean & Structured: All data professionally format

Formula 1 Racing Dataset (1950-2023)

Context:

Formula 1 (F1 or Formula One) is the pinnacle of single-seater auto racing, governed by the Fédération Internationale de l'Automobile (FIA) and managed by the Formula One Group. The FIA Formula One World Championship, established in 1950, is renowned globally for its high-speed, high-technology racing. The "formula" in Formula One signifies the stringent set of rules that all participating cars must adhere to. Each Formula One season comprises a series of races known as Grands Prix, held on specialized circuits and public roads around the world.

About the Dataset:

This dataset encompasses comprehensive information on Formula 1 races, drivers, constructors, qualifying sessions, circuits, lap times, pit stops, and championships from the inaugural season in 1950 up to the latest season in 2024. It serves as a rich repository for analyzing the history, trends, and performance metrics in Formula 1 racing.

Dataset Details:

-**Season List:** Details the seasons of the Formula 1 World Championship from 1950 to 2023.

-**Race Schedule:** Lists the schedule of races for each season, including dates and locations.

-**Race Results:** Provides the results of each race, including positions, points, and timings.

-**Qualifying Results:** Details the results of qualifying sessions for each race, determining starting grid positions.

-**Sprint Qualifying Results:** Results of sprint qualifying sessions introduced in recent seasons to determine starting positions for the race.

-**Standings:** Provides the championship standings for drivers and constructors at various points in the season.

-**Driver Information:** Detailed information about drivers participating in the F1 seasons.

-**Constructor Information:** Information about the teams (constructors) competing in Formula 1.

-**Circuit Information:** Details about the circuits used for the Grands Prix, including layout and specifications.

-**Finishing Status:** Describes the status of drivers at the end of each race (e.g., finished, retired, disqualified).

-**Lap Times:** Recorded lap times for each driver during races, useful for performance analysis.

-**Pit Stops:** Data on pit stops made during races, including timings and reasons.

Key Features:

• Historical Coverage: Spans over 70 years of Formula 1 history, from 1950 to 2024.
• Comprehensive Data: Includes a wide range of information from race results to pit stop timings.
• Global Reach: Covers Grands Prix held in various countries across different continents.
• Performance Metrics: Detailed lap times and pit stop data for in-depth performance analysis.
• Team and Driver Insights: Information on all major drivers and constructors, including their career statistics and historical performance.

Usage:

This dataset is valuable for:

• Historical Analysis: Studying the evolution of Formula 1, including technological advancements and changing competitive dynamics.
• Performance Analysis: Analyzing driver and team performance over different periods and conditions.
• Strategy Development: Understanding pit stop strategies and their impact on race outcomes.
• Educational Purposes: Serving as a resource for teaching data analysis, sports analytics, and engineering.
• Fan Engagement: Providing detailed insights for Formula 1 enthusiasts and historians.

Data Maintenance:

• Last Updated: July 6, 2024

Additional Notes:

• Data Source: Compiled from https://ergast.com/mrd/db/ , a reliable and comprehensive source of motor racing data.
• Data Quality: Efforts have been made to ensure the accuracy and completeness of the dataset, with ongoing updates and revisions.
• Usage Rights: The dataset is available for public use, adhering to the data source's usage guidelines.

McLaren received an estimated *** million U.S. dollars in prize money in 2024, with the team winning the Constructors' Championship for the ***** time. Meanwhile, second-placed Ferrari received around *** million U.S. dollars in prize money.

About Formula 1 (commonly known as F1 or Formula One) stands as the pinnacle of single-seater auto racing, governed by the Fédération Internationale de l'Automobile (FIA) and operated by the Formula One Group. Since its debut in 1950, the FIA Formula One World Championship has established itself as one of the most prestigious motorsport competitions globally. The term "formula" refers to the technical regulations that all competing cars must adhere to. Each season comprises a series of races called Grands Prix, held on dedicated racing circuits and street courses around the world.

What's Included: We've compiled a comprehensive dataset covering every aspect of Formula 1 from 1950 through the current 2025 season. This includes detailed records on races, drivers, constructors, qualifying sessions, circuits, lap times, pit stops, and championship standings. The 2025 season—marking F1's 75th anniversary—features a record-tying 24 Grands Prix spanning 21 countries across five continents, along with six Sprint race weekends

            Formula 1 - Belgian, Hungarian, Dutch Grand Prixes 

Context

Belgian Grand Prix

The Belgian Grand Prix is a motor racing event which forms part of the Formula One World Championship. The first national race of Belgium was held in 1925 at the Spa region's race course, an area of the country that had been associated with motor sport since the very early years of racing.

• Circuit length: 7.004 km (4.352 miles)

• First held: 1925

• Laps: 44

• Most wins (constructors): Ferrari (18)

• Most wins (drivers): Michael Schumacher (6)

• Number of times held: 81

Hungarian Grand Prix

The Hungarian Grand Prix is a motor racing event held annually at the Hungaroring racetrack located on the northeastern outskirts of Budapest, within the village of Mogyoród.

• Circuit length: 4.381 km (2.722 miles)

• First held: 1936

• Laps: 70

• Most wins (constructors): McLaren (13)

• Most wins (drivers): Lewis Hamilton (8)

• Number of times held: 41

Dutch Grand Prix

The Dutch Grand Prix is an annual Formula One World Championship auto racing event, held at Circuit Zandvoort, North Holland, the Netherlands, from 1950 through 1985, and after a 35 year hiatus, from 2021 to 2026.

Circuit length: 4.259 km (2.646 miles)

• First held: 1950

• Laps: 72

• Most wins (constructors): Ferrari (8)

• Most wins (drivers): Jim Clark (4)

• Number of times held: 36

Circuits

Spa-Francorchamps

Hungaroring

Circuit Zandvoort

Content

The dataset consists of all information on the Formula 1 Canada, Austria and UK Grand Prixes 2025, drivers, constructors, qualifying, lap times, and pit stops.

• Time period: July - August 2025

Acknowledgements

The data is fetched using Fast F1 Package.

Inspiration to Use This Dataset

This dataset offers a unique opportunity to explore three historic and technically diverse circuits—Spa’s high-speed drama, Hungaroring’s tight and twisty layout, and Zandvoort’s fast, flowing corners. By analyzing drivers, constructors, lap times, and strategies, you can uncover patterns that shaped the 2025 season and build predictive insights for future races.

"To finish first, you must first finish." – Juan Manuel Fangio

            Formula 1 - Canada | Austria | UK Grand Prixes 2025

Context

Canada Grand Prix

The Canadian Grand Prix is an annual motor racing event held since 1961. It has been part of the Formula One World Championship since 1967.

• Circuit length: 4.361 km (2.709 miles)

• First held: 1961

• Laps: 70

• Most wins (constructors): Ferrari (14)

• Number of times held: 60

Austria Grand Prix

he Austrian Grand Prix is a Fédération Internationale de l'Automobile sanctioned motor racing event that was held in 1964, 1970–1987, and 1997–2003. It returned to the Formula One calendar in 2014, where it has remained since. It was first held at the Zeltweg Air Base for its first, non-Championship running.

• Circuit length: 4.326 km (2.688 miles)

• First held: 1963

• Laps: 71

• Most wins (drivers): Max Verstappen (4)

• Number of times held: 44

UK Grand Prix

The British Grand Prix is a Grand Prix motor racing event organised in the United Kingdom by Motorsport UK. First held by the Royal Automobile Club in 1926, the British Grand Prix has been held annually since 1948 and has been a round of the FIA Formula One World Championship every year since 1950.

• Circuit length: 5.891 km (3.66 miles)

• First held: 1926

• Laps: 52

• Most wins (constructors): Ferrari (18)

• Most wins (drivers): Lewis Hamilton (9)

• Number of times held: 80

Circuits

• Circuit Gilles-Villeneuve

• Red Bull Ring

• Silverstone Circuit

Content

The dataset consists of all information on the Formula 1 Canada, Austria and UK Grand Prixes 2025, drivers, constructors, qualifying, lap times, and pit stops.

• Time period: June - July 2025

Acknowledgements

The data is fetched using Fast F1 Package.

Inspiration to Use This Dataset

Dive into the 2025 season's most thrilling races to analyze driver performance, team strategies, and circuit dynamics. This dataset is perfect for building comparative insights, lap-time visualizations, and predictive models across diverse tracks and conditions.

"It’s not always the fastest car that wins, it’s the driver who refuses to lose." – Dale Earnhardt Jr.

Download Free Sample
The motorsport market is expected to grow at a CAGR of 1% during the forecast period. Significant investments by sponsors, drivers.2, and drivers.3 are some of the significant factors fueling motorsport market growth.

Significant investments by sponsors

Motorsports involve organizing competitive race events of motorized vehicles. These events generally involve custom cars, trucks, motorcycles. Formula One (F1), National Association for Stock Car Auto Racing (NASCAR), IndyCar, and the World Rally Championship (WRC) are some of the popular motorsport events. F1 is the most popular among them, closely followed by IndyCar Series and NASCAR. The global motorsport market is segmented by type under the following categories: F1 NASCAR Other motorsports The geographic coverage of the report is global with the following segments: Europe North America APAC South America MEA The exhibit below provides an illustrative view of the offerings that are included to create the market definition of the global motorsport market. This market definition has a significant impact on the market sizing provided in the report.

Context

The 2021 FIA Formula One World Championship is a planned motor racing championship for Formula One cars which will be the 72nd running of the Formula One World Championship. It is recognized by the Fédération Internationale de l'Automobile (FIA), the governing body of international motorsport, as the highest class of competition for open-wheel racing cars. The championship is due to be contested over twenty-three Grands Prix, which will be held around the world. Drivers and teams are scheduled to compete for the titles of World Drivers' Champion and World Constructors' Champion respectively.

F1 Fantasy allows F1 fans to choose a team consisting of 5 F1 drivers and an F1 constructor, with a maximum spending limit of $100M. Each driver and constructor has a certain value, which changes based on previous performance. Based on performance of drivers and constructors selected in a team, the fan earns points. Fans can create or join leagues to compete with other fans. At the end of the season, the fan with the most number of points earned wins his/her respective league.

For more information on Formula 1: Formula 1 Official Website

For more information of Formula 1 Fantasy: F1 Fantasy Official Website

Content

The dataset includes CSV files for driver values and constructor values. Each file contains the F1 Fantasy value for each driver and constructor, respectively, before the start of each grand prix. Along with these files, constructors' and drivers' performance at each grand prix is also recorded.

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Context

The Japanese Grand Prix is a motor racing event in the calendar of the Formula One World Championship.

The dataset was generated using the FastF1 API by loading the Japanese Grand Prix race session (denoted as "R") for the 2025 season. Instead of directly accessing a dedicated pit stop attribute (which might not exist in some versions of the API), the data is obtained by filtering the laps data to identify laps that include pit stop times. This approach leverages the 'PitOutTime' field to capture when pit stops occurred during the race.

The context of this data is rooted in understanding the in-race strategies related to pit stops, which can greatly influence race outcomes. Teams and analysts use such information to assess the efficiency of pit stop timings and their impact on overall performance.

Circuit length: 5.807 km (3.608 miles)

First held: 1963

Laps: 53

Most wins (constructors): McLaren (9)

Most wins (drivers): Michael Schumacher (6)

Number of times held: 50

Content

The dataset consists of all information on the Formula 1 Japanese Grand Prix 2025, drivers, constructors, qualifying, lap times, and pit stops.

Time period: 4 - 6 April 2025

Acknowledgements

The data is fetched using Fast F1 Package.

Inspiration to Use This Dataset

Unlock strategic insights and race trends from the Japanese Grand Prix to fuel your data analysis, visual storytelling, or predictive modeling in motorsport analytics.

"Racing is life. Anything before or after is just waiting." – Steve McQueen

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The Formula 1 World Championship History (1950-2024) dataset offers a comprehensive exploration of Formula 1 racing, spanning over seven decades of competition. This dataset is designed to be a valuable resource for those interested in the evolution of Formula 1, covering every aspect of the sport from its inception in 1950 through the latest 2024 season.

Context

Formula 1, also known as F1, represents the pinnacle of single-seater auto racing. Governed by the Fédération Internationale de l'Automobile (FIA), it has grown into a globally recognized sport, drawing millions of fans around the world. The sport is known for its high-speed races, strategic depth, and the relentless pursuit of excellence both on the track and in the design and engineering of the cars.

This dataset captures the rich history of Formula 1 by providing detailed information on races, drivers, constructors (teams), circuits, and more. Whether you're a motorsport enthusiast, data analyst, or historian, this dataset is an excellent tool for deep diving into the data and uncovering insights about the sport.

Contents of the Dataset

The dataset is organized into multiple CSV files, each offering detailed data on different aspects of Formula 1:

1) Track_Information.csv: Contains details about the circuits where races have been held, including their location, length, and unique characteristics.

2) Team_Details.csv: Provides information about the constructors, including their history, achievements, and performance across different seasons.

3) Constructor_Performance.csv: Details the performance of constructors in individual races, showing how teams have evolved over the years.

4) Constructor_Rankings.csv: Offers annual standings of constructors, highlighting the competitive dynamics within the sport.

5) Driver_Details.csv: Includes comprehensive information about the drivers, such as their personal details, career statistics, and achievements.

6) Driver_Rankings.csv: Displays the yearly standings of drivers, showcasing who topped the charts and how tight the championship battles were.

7) Race_Schedule.csv: Lists all the races held from 1950 to 2024, along with details such as the date, location, and race name.

8) Race_Results.csv: Provides detailed results of each race, including finishing positions, points earned, and other key metrics.

9) Lap_Timings.csv: Contains data on lap times recorded by drivers during races, giving insights into their performance consistency.

10) Pit_Stop_Records.csv: Offers information on pit stops made during races, including timing and strategy, which often impact the outcome of the race.

11) Qualifying_Results.csv: Details the results of qualifying sessions, which determine the starting grid for each race.

12) Sprint_Race_Results.csv: Includes data on sprint races, shorter races introduced to decide starting positions for the main race.

13) Season_Summaries.csv: Summarizes each season, including the number of races, champions, and key moments.

14) Race_Status.csv: Contains codes and descriptions related to the status of cars during a race, such as if a car finished, retired, or was disqualified.

Data Source

The dataset is sourced from the Ergast Motor Racing Data API, a trusted and frequently updated source for Formula 1 data. The API has been reliably providing data over the years, ensuring the dataset is accurate and up-to-date. Each file in this dataset has been carefully compiled to offer a thorough overview of Formula 1’s history, making it an essential resource for anyone interested in the sport.

Potential Use Cases

This dataset is versatile and can be used for a wide range of applications, including:

1) Historical Analysis: Study the changes and trends in Formula 1 over the years, including the evolution of teams, drivers, and circuits.

2) Performance Trends: Analyze trends in driver and constructor performances, and identify key factors contributing to success in Formula 1.

3) Race Strategy Insights: Investigate the impact of different strategies, such as pit stops and qualifying positions, on race outcomes and championships.

4) Data Visualization: Create compelling visualizations to illustrate the history and key events in Formula 1, such as championship rivalries, race victories, and more.

5) Fan Engagement: Engage with Formula 1 fans by providing them with in-depth data-driven insights about their favorite teams, drivers, and races.

The Formula 1 World Championship History (1950-2024) dataset is a rich and detailed resource that offers extensive insights into one of the world’s most popular motorsports. Whether you are looking to conduct in-depth analysis, create visualizations, or simply explore the history of Formula 1, this dataset is an excellent starting point. ...

Context

The Chinese Grand Prix is a round of the Formula One World Championship. The event was held every year from 2004 until 2019 before it was suspended from 2020 to 2023 due to the COVID-19 pandemic in China. The event resumed in 2024 and is contracted to be held until 2030.

The dataset was generated using the FastF1 API by loading the Chinese Grand Prix race session (denoted as "R") for the 2025 season. Instead of directly accessing a dedicated pit stop attribute (which might not exist in some versions of the API), the data is obtained by filtering the laps data to identify laps that include pit stop times. This approach leverages the 'PitOutTime' field to capture when pit stops occurred during the race.

The context of this data is rooted in understanding the in-race strategies related to pit stops, which can greatly influence race outcomes. Teams and analysts use such information to assess the efficiency of pit stop timings and their impact on overall performance.

Circuit length: 5.451 km (3.387 miles)

First held: 2004

Laps: 56

Most wins (constructors): Mercedes (6)

Most wins (drivers): Lewis Hamilton (6)

Number of times held: 18

Content

The dataset consists of all information on the Formula 1 Chinese Grand Prix 2025, drivers, constructors, qualifying, circuits, lap times, and pit stops.

Time period: 21 - 23 March 2025

Acknowledgements

The data is fetched using Fast F1 Package.

Inspiration

This dataset allows F1 enthusiasts, analysts, and data scientists to explore race strategies, driver performance, and historical trends in the Chinese Grand Prix. By analyzing pit stops, lap times, and standings, you can uncover patterns that influence race outcomes and even build predictive models for future races.

"To achieve anything in this game, you must be prepared to dabble on the boundary of disaster." – Stirling Moss

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https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F76fa42441e6edaf5e1106a66130b0707%2FScreenshot%202025-03-25%20163922.png?generation=1742902799341200&alt=media" alt="">

Context

Formula One (also known as Formula 1 or F1) is the highest class of international single-seater auto racing sanctioned by the Fédération Internationale de l'Automobile (FIA) and owned by Liberty Media, an American mass media company, through its wholly owned subsidiary, the Formula One Group. The World Drivers' Championship, which became the FIA Formula One World Championship in 1981, has been one of the premier forms of racing around the world since its inaugural season in 1950. The word "formula" in the name refers to the set of rules to which all participants' cars must conform. A Formula One season consists of a series of races, known as Grands Prix (French for ''grand prizes' or 'great prizes''), which take place worldwide on purpose-built circuits and on public roads.

Content

This dataset contains the data of all the formula one grand prix held since 1950. It also includes race results as well as driver standings of each season and constructors standing.

Inspiration

Data collection and analysis is one of the most vital part of formula 1 races. On the basis of the data collected by each team, they develop their own car, to innovate the technology they use. Without real-time data analytics, the F1 we know today could be very different. Hundreds of data points per second are generated in all key areas of the cars. Meanwhile, engineers and data analyst both at the circuit and in the factories (which are thousands of miles away), compete to extract as much information as possible and weaponize that information in order to fight for victory.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14466026%2F49816493df89627d196532b767eeff16%2Fa.jpg?generation=1746654444817618&alt=media" alt=""> Context The Bahrain Grand Prix is a motor racing event in the calendar of the Formula One World Championship.

The dataset was generated using the FastF1 API by loading the Bahrain Grand Prix race session for the 2025 season. Instead of directly accessing a dedicated pit stop attribute (which might not exist in some versions of the API), the data is obtained by filtering the laps data to identify laps that include pit stop times. This approach leverages the 'PitOutTime' field to capture when pit stops occurred during the race.

The context of this data is rooted in understanding the in-race strategies related to pit stops, which can greatly influence race outcomes. Teams and analysts use such information to assess the efficiency of pit stop timings and their impact on overall performance.

Circuit length: 5.412 kilometers (3.363 miles)

Laps: 57

Most wins (constructors): McLaren (194)

Most wins (drivers): Oscar Piastri

Number of times held: 21

Content The dataset consists of all information on the Formula 1 Bahrain Grand Prix 2025, drivers, constructors, qualifying, lap times, and pit stops.

Time period: 11 - 13 April 2025

Acknowledgements The data is fetched using Fast F1 Package.

Inspiration to Use This Dataset Unlock strategic insights and race trends from the Japanese Grand Prix to fuel your data analysis, visual storytelling, or predictive modeling in motorsport analytics.

"Racing is life. Anything before or after is just waiting." – Steve McQueen

Context

The FIA Formula 2 Championship is a second-tier single-seater championship organized by the Fédération Internationale de l'Automobile (FIA). The championship was introduced in 2017, following the rebranding of the long-term Formula One feeder series GP2.

In addition to being the championship that awards the most points for the FIA Superlicence, it has been the previous step for many F1 drivers such as Charles Leclerc, George Russell, Lando Norris, Yuki Tsunoda, Guanyu Zhou and others.

Content

The information is divided into 5 files that correspond to each event. Not all events take place every week (for example the sprint race). The columns are:

• LAPS: Total laps traveled by the pilot
• TIME: Total event time
• GAP: Distance from pilot to race leader (seconds)
• INT: Distance to the next pilot (seconds)
• KPH: Speed (mean)
• BEST: Best time lap
• LAP: Lap for the best time
• POS: Final position of the event
• CAR: Car number
• PILOT NAME: Pilot name
• TEAM: Constructor
• CIRCUIT: Circuit name
• TYPE: Type of the event
• ROUND: Round (from 1 to total races per season)
• DATE: Date of the feature race
• LAP SET ON: Hot lap (fastest) for the Quali
• QUALI TYPE: multi-session indicator of quali

Collection Methodology

You can visit the source code of the data pipeline in GitHub

Context

Formula One (also Formula 1 or F1 and officially the FIA Formula One World Championship) is the highest class of single-seat auto racing that is sanctioned by the Fédération Internationale de l'Automobile (FIA). The FIA Formula One World Championship has been one of the premier forms of racing around the world since its inaugural season in 1950.

Content

This dataset contains data from 1950 all the way through the 2017 season, and consists of tables describing constructors, race drivers, lap times, pit stops and more.

Acknowledgements

The data was downloaded from http://ergast.com/mrd/ at the conclusion of the 2017 season. The data was originally gathered and published to the public domain by Chris Newell.

Inspiration

I think this dataset offers an exciting insight into a $ billion industry, enjoyed by hundreds of millions of viewers all over the world. So please, explore and enjoy!

Context

A single table of the prominent data regarding all Formula E races, derived from Wikipedia race reports.

Content

This data set is merely a single CSV file, backed with all the files I used to create it. This is taken purely from Wikipedia race reports, with some R code to parse the relevant results tables and clean things up.

So while 57 files are available (as of Version 1), the main output file, as shown in the preview is the intended data set to use.

It has not been denormalized, so in it we have race, driver, team, and results information. Race: season, race number, race date, and race name Driver: name Team: car number, team name from Wikipedia, continuity-based team name Results: two forms of rank, grid start, number of laps, report time/retirement message, the points awarded, and the three categories of points

Acknowledgements

Wikipedia's race reports are consistent enough that a couple hours of cleanup was all that was needed to derive this data set. A big thanks is owed to the contributors there. Motorsports Stats information is a bit more expansive and possibly simpler to parse, but I used Wikipedia to keep licensing as simple as possible.

Inspiration

The inspiration for adding this to Kaggle was that it begs a comparison to Formula 1. @vopani has posted the ergast.com data set, and its accessibility had me able to work with the data enough to do some simple predictions. I have not found a Formula E data set that provides the results in one place. Unfortunately I don't know of a source for lap times at all. But with Formula E continually branding themselves as one of the most unpredictable championships in racing, putting this data in Kaggle seemed useful. It's my first true data set, and it's nice to give back to a community I've been part of for so long.

So I aim to add a few notebooks here soon to start this out. I also aim to manually keep it updated through the flurry of Berlin races to finish Season 6, ideally the night following each race using hand-entered results.

Data that is available that I have chosen not to use would be a deeper dive into Qualifying results, and potentially practice times. The qualifying results are already in the HTML pages I've posted here, they'd just need to be parsed. But even with that data in hand with the F1 data set, I have yet to use it other than pre-penalty grid positions. For those that don't know, Formula E's qualifying introduces a negative feedback loop, in that the top 6 of the Championship are forced to qualify in the first group, where the track is frequently very dirty/dusty and has less grip. It is rare that a driver from Group 1 makes it to super pole. And listening to the commentators, they frequently will comment on who "looked fast in practice" so if you had that information it might help predict race finish.