Dataset Description: F1 Races 2000-2024

Overview

This dataset consolidates data from Formula 1 races between 2000 and 2024, designed to facilitate predictive modeling and analytical tasks related to race outcomes. The dataset integrates information from multiple reliable sources, including the Ergast API, VisualCrossing API, and Wikipedia, enriched through feature engineering techniques to enhance its predictive power.

Data Sources

• Ergast API: Provides historical race data including driver and constructor statistics, race results, qualifying times, and more.
• VisualCrossing API: Offers detailed weather data, including precipitation information during race events.
• Wikipedia: Supplies circuit-specific details like the number of turns and track length through automated web scraping.

Dataset Structure

The dataset includes comprehensive race-related attributes categorized as follows: - Race Information: Year, round, circuit ID, and weather conditions. - Driver & Constructor Details: IDs, performance metrics, historical standings, and nationality. - Race Metrics: Grid position, lap times, pit stops, status, and final positions. - Engineered Features: Derived variables such as driver and constructor podium finish percentages, average positions, weighted probabilities based on circuit characteristics, and recent performance trends.

Key Features

• Top 3 Finish: Binary target variable indicating if a driver finished in the top 3.
• Weather Conditions: Binary indicator for rainy conditions during the race.
• Performance Metrics: Historical and seasonal averages for both drivers and constructors.
• Track Characteristics: Number of turns and track length for each circuit.

Potential Use Cases

• Predictive Modeling: Classify and predict podium finishes using machine learning algorithms.
• Performance Analysis: Evaluate driver and constructor performance trends over multiple seasons.
• Feature Engineering Practice: Apply advanced techniques to create new predictive features.

Technical Details

• Data Format: CSV
• Total Records: Over 9,800 race results
• Missing Values: Managed through strategic imputation methods, especially for rookie drivers and new race entries.
• Class Imbalance: Addressed using SMOTE during modeling to ensure balanced predictive outcomes.

Acknowledgments

Special thanks to the contributors of the Ergast API, VisualCrossing API, and the Wikipedia community for providing essential data points that made this dataset possible.

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.

The dataset is a comprehensive, structured collection of historical Formula 1 race data compiled from the Ergast API. It is organized in a relational format across multiple CSV files, each capturing a different aspect of the sport. The `races.csv` file includes metadata on each race such as date, circuit, and season. The `results.csv` file provides final race outcomes for every driver, while `qualifying.csv` contains qualifying session results. `lap_times.csv` and `pit_stops.csv` offer granular, session-level data for each driver’s performance throughout the race. Additional files such as `drivers.csv` and `constructors.csv` provide biographical and team-related information, while `constructor_standings.csv`, `driver_standings.csv`, and `constructor_results.csv` track season-long performance. Files like `circuits.csv`, `status.csv`, and `seasons.csv` provide supporting metadata that enhances the usability and relational structure of the dataset. This dataset is well-suited for time series analysis, predictive modeling, performance evaluation, and motorsport analytics.

In 2025, younger adults in the United States tended to have more interest in Formula One, with ** percent of people aged between 18 and 29 following the racing series closely. Meanwhile, only ***** percent of those aged 65 and over did the same.

In 2025, *********** adults in the United States followed Formula One to some extent. Meanwhile, ** percent of respondents said that they did not follow the racing series closely.

Dataset

This dataset was created by Mateo Pineda Giraldo

Released under Apache 2.0

Contents

Context

F1 data, race by race, from 1983 onwards

Content

Each folder contains data for each race of that season

Acknowledgements

Collected using data available in public domain

Inspiration

My main goal is to come up with a predictive model for F1 races

In a September 2024 survey, ** percent of respondents in the United States identified as Formula 1 fans. Meanwhile, ** percent of respondents described themselves as diehard fans.

The Formula 1 Racing market, synonymous with high-octane excitement and cutting-edge technology, has grown into a multi-billion-dollar industry that captivates millions of fans worldwide. With a rich history dating back to 1950, Formula 1 has evolved not only as a thrilling motorsport but also as a significant busin

In 2024, the total revenue of the Formula One Group amounted to around **** billion U.S. dollars, representing an increase of over 13 percent on the previous year. Since 2017, the group has been owned by Liberty Media Corporation.

A November 2022 survey in Great Britain revealed that around ** percent of avid Formula One fans in the country were male. Meanwhile, ** percent of British F1 fans were female.

Max Verstappen's Full Race Car Data: 2024 Miami Grand Prix

  Data Source

Data obtained using the fastf1 API, ensuring reliability and accuracy in the collected data.

  Metrics

The dataset comprises a comprehensive range of metrics crucial for analyzing Max Verstappen's performance during the 2024 Miami Grand Prix, including:

Data: Timestamps for each recorded data point. RPM: Engine revolutions per minute, indicating engine performance and power delivery. Speed:… See the full description on the dataset page: https://huggingface.co/datasets/Draichi/Formula1-2024-Miami-Verstappen-telemetry.

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• Formula One (also known as Formula 1 or F1) is the highest class of international auto racing for single-seater formula racing cars sanctioned by the Fédération Internationale de l'Automobile (FIA). 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, which take place worldwide on both purpose-built circuits and closed public roads.

• The craze for F1 among the fans is astonishing, which has been creating quite a buzz in major social media platforms like Twitter. The dataset brings you such tweets posted with the #f1 hashtag.

Information regarding the data

• The data totally consists of 50k+ records with 13 columns. The collection started on 25/7/2020 and will be updated regularly. The description of the features is given below.

Inspiration

    "I am an artist, the track is my canvas and the car is my brush." – Graham Hill

This data was recorded during Flight 1 of the Blue Origin Deorbit, Descent, and Landing Tipping Point (BODDL-TP) Game Changing Development (GCD) Program. The flight included IMU, cameras for terrain relative navigation, and range and velocity lidar sensors. The flight was completed under NASA contract 80LARC19C0005 in October 2020.

Financial overview and grant giving statistics of The F1 Key Foundation

In 2024, the attendance of the British Grand Prix amounted to around 480,000, making it the best-attended F1 race of that year. Meanwhile, the attendance of the Australian Grand Prix totaled over 450,000.

Context

FIA F1 - Formula 1 is a Cutthroat motorsport competition started since 1950 and continues till date and attracts more and more fans every year towards this heritage sport.

Content

I have included the various datasets like Race wins, Constructors as well as Drivers Championship and Fastest Laps for years 1950-2019 and will add more and recent data shortly soon as it is available.

The survey interviewed 254 retailer shops in 10 sub-cities of Addis Ababa. 30 supermarkets, 20 mini-markets, 100 regular shops, 80 dairy shops and 24 open market shops selling dairy products were interviewed. Details of the sampling strategy is found in the attachment. The survey collected information on the characteristics of the shop, details of dairy products sold, prices and quality. Policy makers, research, and other stakeholders can use this data to analyses dairy value chain in Ethiopia and dairy retailing practices in Ethiopia. This data set was collected through research of the project “Improving the evidence and policies for better performing livestock systems in Ethiopia” lead by the International Food Policy Research Institution as part of the Feed the Future Innovation Lab for Livestock Systems.

Posanapalle V2 F1 Ortho Data

## Overview

Posanapalle V2 F1 Ortho Data is a dataset for instance segmentation tasks - it contains Palm Tree Crown annotations for 273 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Formula One is a motorsport discipline sanctioned by the FIA and owned by Formula One group. In a survey conducted in ********, around ** percent of Hispanic respondents in the United States were avid fans of Formula One.