samarth745 / formula1-data-analysis

Detailed Analysis on Formula 1 past races

Formula One Analysis

Introduction:

Formula One (F1) stands as the pinnacle of international racing, governed by the Fédération Internationale de l'Automobile (FIA). Since its inception in 1950, the FIA Formula One World Championship has enthralled racing enthusiasts worldwide. This project conducts a descriptive analysis of Formula 1 races to unveil significant trends concerning drivers and constructors over the years.

Project Goal:

The primary objective is to extract insights from historical F1 races. By leveraging Exploratory Data Analysis (EDA), this project aims to uncover patterns and correlations within the dataset.

Skills Used:

• Exploratory Data Analysis (EDA)

Libraries Used:

• Seaborn
• Pandas
• Matplotlib

Terminologies

• Constructor - Constructors are basically teams in F1, Each season has 10 competing constructors. Each constructor has 2 drivers assigned to it.
• Constructors build their own cars to race each season under FIA Norms
  
• Pit Stops - For each race the Formula 1 car is allowed to change tyres and modify certain elements using pit stops. The Pit stop timings does affect race.
  
• Qualifying - There are 3 Qualifying races Q1, Q2 and Q3. Lap times of Qualifying race decides the starting grid(Starting position for each race) of the race
  
• Pole - The first position in the Grid is called pole

Aim of the project is to answer the following questions based on past Data

Data Set

The Data Set is recieved using Kaggle The following are the tables in the data

• circuits - Circuits where F1 races are held
• Constructor_Results - Race results of the constructor's championship
• Constructors_Standing Final standings of the constructor's championship
• Constructors - Constructors in F1
• Driver Standing - Final standings of the driver's championship
• Drivers - Drivers in F1
• Lap times - Lap times in F1
• Pit stops - Pit stops timings in F1
• Qualifying - Qualifying results in F1
• Races Races in F1
• Results - Results of all F1 races
• Seasons - Season wise description F1 Mapping of various statuses
  Identifiers are presents in circuits.csv, driver.csv, constructors.csv , Races.csv and Status.csv where we can identify the circuit, driver, constructor, races and status using their unique ids
  

Data Connections

Analysis

• Distribution of F1 circuits across different countries: This analysis aims to understand the geographical distribution of Formula 1 circuits worldwide. It involves identifying which countries host the most races and how the distribution has evolved over time.

• Identification of top drivers and constructors based on points and podium finishes: This analysis focuses on determining the most successful drivers and constructors in Formula 1 history. It considers factors such as total points earned, number of race wins, and podium finishes.

• Analysis of constructors' performance over time: This analysis tracks the performance of F1 constructors (teams) across different seasons. It examines how constructor standings have evolved over the years and identifies trends in the dominance of certain teams.

• Patterns in the number of races over the years: This analysis explores the historical trends in the number of Formula 1 races held each year. It seeks to identify periods of expansion or contraction in the F1 calendar and understand the factors driving these changes.

• Trends in the nationality and age of drivers: This analysis investigates the demographics of Formula 1 drivers, including their nationalities and ages. It aims to uncover any trends or patterns in driver demographics over time and understand how the sport's diversity has evolved.

• Identification of drivers with the most fastest laps: This analysis focuses on identifying drivers who have recorded the most fastest laps in Formula 1 races. It highlights the skill and consistency of drivers in setting fast lap times during races.

Scorecards:

• Position dashboard for drivers and constructors based on each year
• Driver information search functionality
• Generation of content for missing sessions

Contributing:

Contributions to this project are welcomed! If you have suggestions for improvements or additional features, please feel free to open an issue or submit a pull request.