Formula 1 analysis (1950–2020) using Tableau

Preet Gangrade
10 min readFeb 9, 2021


Formula One is the highest class of international auto racing sanctioned by the Fédération Internationale de l’Automobile (FIA) and owned by the Formula One Group. (aka Formula 1 or F1.F1 has been 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 Grand Prix which take place worldwide on purpose-built circuits and on public roads.

Formula 1 is and has been a favourite sport of mine for a year. Extreme precision, high accuracy and excellent team are the key constituents for a team to participate and win the Grand Prix. The moment anyone decides to act on their own, the team can either lose position or straight away get out of the competition.


For my final visualization I wanted to express my data as an interactive visualization in a fascinating way that leads to learning and insights that could be helpful for a person who is new to the sport and also to someone who has been watching the sport closely.
The interesting thing in the above prompt is that you define the user group as you see, but the visualization must still be clearly designed for “everyone” and not just “someone.”

Through our visualization, I aim to enable exploration of data to help users of all “F1 experience levels” discover and make sense of the data, whether seeking objective information or using the information to form subjective assessments.

  1. Formula 1 2020: Sports Viz Sunday Data Challenge | by James Smith | Nightingale
This was one of the most detailed work I found on the internet, the visualization is made for print media and this would be used to get in-depth information about the sport. The layout of the dashboard is well thought and this helped me ideate on the interactive dashboard I would want to work on.

2. F1 Data Visualization — Jason J Paul

Similarly, I found another visualization and it was interactive as well and the design and layout look sleek which makes it look more professional. This visualization, however, seems slightly difficult to understand for someone who is new to the sport.

These visuals helped me set a base and gave me a clearer idea as to how I would like my data set to be expressed in a fascinating way.


Tableau Public: Free Data Visualization Software

I used Tableau Public to create all the necessary visualizations. Tableau is an interactive visualization tool that lets the user create visualizations using simple drag and drop methods. It also gave me the flexibility to compile my work on a dashboard and publish it on the Tableau website

Data Set

Formula 1 : 1950–2020

I found a data set on Kaggle, a subsidiary of Google LLC, which is an online community of data scientists and machine learning practitioners. The data set includes Race wins, all Race Results, Qualifying Times, Constructors as well as Drivers Championship and Fastest Laps for the years 1950–2020.

Process —

The first step was to understand the data and make sense of it. As I investigated what kinds of questions it might be able to help answer or facilitate the discussion of. Discussing what questions are of interest normally to F1 fans at various levels led us to task-driven assessment and organization of the data set.

Given the size of the dataset, I decided on the various directions we could take to visualize the data. It came down to 4 separate themes targeting different aspects of the sport. For each theme, I wanted the details, discussing various data points needed and defining what each term in a prospective insight question meant. Before framing these questions I thought about the questions I had when I just started following F1, this helped me brainstorm on the themes I wanted to focus on.

  1. Which drivers are the Greatest Of All Time?
  2. Which team/constructors have been dominating?
  3. What tracks are the most exciting?
  4. Differences among the top 2 Formula Teams in the modern F1?

The next step was to import the data into Tableau. Tableau Public was used as a platform to both visualize the data and present the analysis. The desktop version of the software was used to create 6 visualizations of the datasets that included a circle graph, bar graphs and line graphs. The visualizations and analysis were both published on an interactive dashboard on Tableau online.

Initial Dashboard

UX Study

User Group — I was able to define the user group clearly now. My users would be F1 fans who have just been introduced to the sport or want to know more about the sport as well as F1 fans with at least a functional understanding of the sport, meaning that the user understands the rules, context, and basics of the sport at a level which allows them to follow along in an enjoyable manner when seeing an F1 race or coverage of the sport in general. This definition, although covering a large band of people, still helps us design with an understanding of what we can expect of our users’ analytical and F1 specific literacy.

I interviewed two of my friends — Nikil Keerthi (22) and Dhriti Jain(23). Nikil seems to know a lot about Formula 1, he has been following the sport as a kid and has been to a couple of races as well but on the other hand, Dhriti has no knowledge about the sport but at the same time, she wants to learn more about the sport so that she could at least make conversations when people around her are talking about it.

User Tests were done on zoom call — During the test, users were asked to be loud and respond to everything that they see on the screen.

  1. Feel free to explore the dashboard and share your first impression.
  2. Were you able to figure out who is the best player to date in Formula 1 history? (Rate 1–5)
  3. Are you able to compare different drivers? (Rate 1–5)
  4. The most dominating constructor? (Rate 1–5)
  5. Which track hosts the most exciting race? (Rate 1–5)

Once these tasks were completed, I asked their opinions on the dashboard and the visuals related to it and also how I could make the visualization better. I made sure they were honest with me because this would help me make the visualization better.

Design Alternatives

In order to make a compelling but also useful viz of the data, we must keep in mind some other higher-level principles as well. In visualizing data, you are translating data, and as in any expressive, medium, there is room for misrepresentation, as well as bias in the presentation. User Testing helped me a lot in understanding what exactly goes through a user mind and how I could make a change.

Idea #1 : Story through scrolling feature

Initially both the users liked the colors that were used in the visualization however, Dhriti had a hard time understanding what the visuals meant and wasn’t sure if she was right about what she inferred from the visualization. This made me change the complete layout of my dashboard and then turned into a SCROLLY-TELLING dashboard. This transition helped the user understand the data better and more details were added with the explanatory text.

Scrollable Storyboard

Idea #2 : Constructors Positions –

Initially, the constructor’s position was visualized with a line graph, however, it wasn’t clear since the tick marks on the line graphs were small in size and there was no way to adjust it. I decided to change it and found that Tableau doesn’t let its user control the size of tick marks on a line graph. I overlayed the data visualization with another circle graph which would make the positions a lot more discoverable. You could overlay two sheets in Tableau but formatting the sheet color to none- this lets the background of the worksheet stay transparent.

Left — Before | Right — After


Based on the UX study- which turned out to be really helpful. I designed a scrollable interactive dashboard which was on a fixed dashboard with a dimension of 1000×6000. The background was set to be dark and pop colors were used for this visualization as it reflects with Formula 1 racing. The dashboard also had color consistency maintained throughout, the colors were assigned based on the actual colors used by the Formula 1 team/constructor. The fonts that are used are the ones that are used at F1 tracks. This makes the visualization look more appealing. The entire dashboard was designed in a way to look classy/sleek since that’s what F1 tries to achieve as a brand.

I went ahead with a minimal approach as well, I didn’t want users to be loaded with unnecessary information thus when you hover around these graphs you get detailed information regarding the sport. However, it will clearly prompt, direct, and support the user in formulating their argument or coming to their own conclusion (about which driver and/or team is the most dominant, consistent, fastest-rising, greatest of all time, etc).


User Feedback

As soon as the users loaded the viz, they had a very positive reaction and found the visual design to be very appealing and welcoming. The scroll storyboard shifted their concentration on the sport 100% and proved to be the first point of having a totally immersive experience.

As soon as users came across an insight, their first instinct was to discuss it with someone and either acted in confidence saying things like “yes, of course” or completely acted surprised and wanted to explore more and discuss with the other person. The conversational aspect of the visualization acts as an important design goal.

Users faced some issues understanding what different terms meant and this was solved by adding explanatory texts to help novice users. Insightful — Users were able to gain meaningful insights from the visualization. Most of the time, they started using the visualization by searching for a driver they knew if they were familiar with the sport — which proves the utility of the search function and was soon able to gain very personalized insights based on their previous knowledge of the sport.

The interactive elements of the visualization give the user a way to engage with the visualization by customizing its display to answer their own particular questions. Looking to understand something specific or a series of specifics and enabling the investigation of those subjects makes the visualization more relevant to individual users and allows for a richer understanding of F1 through impactful visual manipulation of the data.


Mercedes used the simultaneous success of Lewis Hamilton and Nico Rosberg to build a burning momentum to go toe to toe with the biggest names in the sport.
Lewis Hamilton is currently the real G.O.A.T of Formula 1 racing with 95 wins and has beaten all the previous records. He has been on track since 2007 and has always given the best performance.

Lewis Hamilton — G.O.A.T

Red Bull Racing is the youngest team amongst the first row runners, arriving into Formula 1 in 2005. After some consecutive wins from 2010–2013, they have since been fighting to hold their ground amongst the top teams

Red Bull Racing stats — Selected in Blue color

Ferrari is one of the oldest and top-performing teams ever — with the most number of driver championships, having won more races, more pole positions, fastest laps, with most number of laps than any other team in the history of Formula 1. However, 2020 wasn’t great for Ferrari as it’s position dropped from 2nd to 6th.

Ferrari(red) — One of the most successful team in Formula 1

Mercedes have been dominating the game since quite a while and Red Bull Racing has been trying really hard to get past Mercedes and this is why the visualization comparing both these constructors was really important.

Red Bull Racing vs Mercedes


Overall, I really enjoyed creating these visualizations and conducting analyses on this topic. Both of my UX research participants were incredibly interested in the topic as all since it’s something that might come into their use when gathering data. Using Tableau was not easy at first, however, once I played around with the tools and understood the complete working of the software I was able to identify the restrictions associated with it.

Furthermore, many of the F1 teams that compete employ statistical analysts to analyse race results; however, these are in general kept undisclosed so that teams are able to keep any tactical advantages these analyses offer to themselves. As such, there are only a handful of papers in the public domain that have done a systematic statistical analysis of F1 race results, and these are focused on the question of who is the best driver and do not consider the question of how much teams and drivers matter in different contexts. It would be interesting to get into the dept of the sport and understand the different forces that come into play.