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https://github.com/ari-s-123/f1-dataviz-fall-2024

A data visualization project meant to analyze and visualize the evolution of key performance metrics in Formula One (F1) over the period from 1950 to 2023.
https://github.com/ari-s-123/f1-dataviz-fall-2024

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A data visualization project meant to analyze and visualize the evolution of key performance metrics in Formula One (F1) over the period from 1950 to 2023.

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# Visualizing the Evolution of Formula One Performance Metrics (1950-2023)



By [Aritra Saharay](https://ari-s-123.github.io/Personal-Site)



Formula One (F1) stands as the pinnacle of motorsport, epitomizing the fusion of

cutting-edge automotive technology, driver prowess, and strategic team

management. Since its inception in 1950, the sport has undergone profound

transformations influenced by technological advancements, regulatory changes,

and evolving competitive dynamics. In recent years, significant developments

such as the introduction of hybrid power units in 2014, the implementation of

budget caps in 2021 to level the competitive field, and adaptations due to

global events like the COVID-19 pandemic have further reshaped the landscape of

F1.



The primary objective of this project is to analyze and visualize the evolution

of key performance metrics in Formula One (F1) over the period from 1950

to 2023. By examining metrics such as mean career win rates for drivers and

points per race for constructors, the project aims to uncover trends, assess the

impact of regulatory changes, and highlight significant shifts in team and

driver performances. These visualizations will provide valuable insights for

fans, analysts, and teams to understand the dynamics that have shaped the sport

over the decades.



### Specific Aims:



- Trend Analysis: Examine how drivers' and constructors' performance metrics

  have evolved over different eras of F1.

- Impact Assessment: Determine the influence of major regulatory changes and

  technological advancements on performance outcomes.

- Comparative Insights: Identify teams and drivers that have shown significant

  improvements or declines in performance.

- Interactive Exploration: Develop interactive visualizations that allow users

  to explore the data in-depth through various analytical lenses.



### Main Insights:



- Mercedes has the best (lowest) mean finishing position in a given season of

  all time of 3.17 in 2017.

- Zakspeed has the worst (highest) mean finishing position in a given season of

  all time of 35.03 in 1989. Funnily enough, 1989 was also the last year they

  participated in F1...

- Out of the drivers with at least 20 race starts, Juan Manuel Fangio appears to

  be the driver with the best mean career finishing position of all time with an

  average finishing place of 4.79. However, it must be noted that the next best

  driver according to this metric is Lewis Hamilton at 4.88, a driver with a far

  greater total number of race starts at 332 compared to Fangio's 58.

- Out of the drivers with at least 20 race starts, Bernd Schneider appears to be

  the driver with the worst mean career finishing position of all time with an

  average finishing place of 28.5. Interestingly, he drove for Zakspeed in 1989,

  the same team that we previously identified as having the worst mean finishing

  position for a constructor in a single year...



### Data sources and abstraction



**Dataset Type:** The project utilizes static multivariate tabular datasets in

CSV format encompassing historical Formula One data from 1950 to 2023. These

datasets include comprehensive information on drivers, constructors, race

results, etc. This is an author-maintained dataset with a universal public

domain license from Kaggle, sourced from the Ergast F1 API updated to July 2024

results. I downloaded a snapshot of this data in October of 2024. The incomplete

2024 season data has been filtered out, and results from sprint races and

Indianapolis 500 have not been considered.



**Data Description for Key Tables:**



- Drivers Table:



  - Variables: driverId, driverRef, number, code, forename, surname, dob,

    nationality, url



- Constructors Table:

- Variables: constructorId, constructorRef, name, nationality, url



- Results Table:

- Variables: resultId, raceId, driverId, constructorId, number, grid, position,

  positionText, positionOrder, points



- Races Table:

- Variables: raceId, year, round, circuitId, name, date, time, url, fp1_date,

  fp1_time



**References:**



- Primary Dataset:

  [Formula 1 World Championship - Kaggle](https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2020)

- Regulatory Changes: Compiled from official FIA publications and reputable news

  sources.