2024: Week 29 - Formula 1 Qualifying Special

Challenge by: Dan Wade

Dan is part of Data School cohort DS42 and has recently set himself a challenge to create 24 Formula 1 vizzes, one for each race week of the season. Naturally, this means he's had to do a lot of data preparation so he's been very generous to also prepare a challenge for our Sports themed July. Over to Dan to explain the challenge.

In Formula 1, each driver participates in a qualifying session which sets the starting grid for the race. A good qualifying result can give a driver a better starting position, which can be crucial for a strong race performance and being in front reduces the risk of being involved in accidents.

Qualifying is made up of three rounds, Q1, Q2 and Q3. It begins with Q1, where all 20 drivers compete for 18 minutes; the five slowest are eliminated and placed in positions 16-20. Q2 follows, lasting 15 minutes, with the remaining 15 drivers vying for the fastest times; the five slowest are then eliminated and placed in positions 11-15. Finally, in Q3, the top 10 drivers from Q2 compete for 12 minutes to set the fastest lap times, with the quickest driver earning pole position and the others filling positions 2-10.

The data set contains every single lap-time recorded in the session including all warm-ups and cool down laps. Your task is to arrange this data to produce an output of
the final qualifying result.

Tip: You can identify when a session ends and another starts by observing a larger gap in start times

Inputs

The data this week comes from the OpenF1 API.
  1. Qualifying Lap Times 
  2. Drivers and Constructor Details 

Requirements

  • Input the data
  • Clean the date_start field to make it useable as a date
  • Identify Q1 times
    • Identify the earliest date_start of the session (This is shortly after Q1 began)
    • Calculate when Q1 ended using the guidelines above
      • Allow an extra 2 minutes to catch all drivers
    • Filter to keep only Q1 times
      • Identify the quickest lap time per driver
      • Rank the drivers AND filter to keep only the 5 slowest (P16-P20)
  • Identify Q2 times
    • Filter out Q1 times
    • Identify the earliest date_start of the session
    • Calculate when Q2 ended using the guidelines above 
      • Allow an extra 2 minutes to catch all drivers
    • Use a calculated field to identify Q2 times and Q3 times
    • Filter to keep only Q2 times
      • Identify the quickest lap time per driver
      • Rank the drivers AND filter the 5 slowest (P11-P15)
  • Identify Q3 times
    • From the calculated field where you identified Q3 times, filter out the Q2 times
    • Identify the quickest lap time per driver
    • Rank the drivers (P1-P10)
  • Union Q1, Q2 and Q3 together
  • Join to your driver data
  • Sort to get the correct order for the output
  • Output the data

Output

  • 6 fields
    • Position
    • driver_number
    • driver_code
    • driver_name
    • constructor_sponsor_name
    • lap_duration
  • 20 rows (21 including headers)
You can view the outputs here.

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

Popular posts from this blog

2024: Week 1 - Prep Air's Flow Card

Image
Created by: Carl Allchin Welcome to a New Year of Preppin' Data challenges. For anyone new to the challenges then let us give you an overview how the weekly challenge works.  Each Wednesday the Preppin' crew (Jenny, myself or a guest contributor) drop a data set(s) that requires some reshaping and/or cleaning to get it ready for analysis.  You can use any tool or language you want to do the reshaping (we build the challenges in Tableau Prep but love seeing different tools being learnt / tried). Share your solution on LinkedIn, Twitter/X, GitHub or the Tableau Forums Fill out our tracker so you can monitor your progress and involvement The following Tuesday we will post a written solution in Tableau Prep (thanks Tom) and a video walkthrough too (thanks Jenny) As with each January for the last few years, we'll set a number of challenges aimed at beginners. This is a great way to learn a number of fundamental data preparation skills or a chance to learn a new tool — New Year...
Read more

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will u...
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in ...
Read more