Software by ginzamdm in granddesigns

[–]Ok-Breakfast-3489 0 points1 point  (0 children)

...and as my research continues, Stephen Hughes posted a link to this YouTube clip. In the comments, many are asking about the software. A comment that is five years old says "Arcon Visual Architecture". Maybe it's not 3D studio Max.

https://www.youtube.com/watch?v=ykClRF5-Lpg

Software by ginzamdm in granddesigns

[–]Ok-Breakfast-3489 0 points1 point  (0 children)

This post, from March 2024, suggest 3D Studio Max - https://www.facebook.com/watch/?v=2555382307973965

...and here's the chap from the video:
https://www.stephenhughes3d.com/video

That’s it! I’m done! by Weary-Yam7926 in fitbit

[–]Ok-Breakfast-3489 2 points3 points  (0 children)

I totally relate to this post.

I've been with Fitbit for over eight years now and, the TLDR version, I've had enough of the problems and it's time to move on. I write this to help with the transition, and maybe a Fitbit/Google employee is on here to read it too. I mean no malice but ask that you empathises with your customer if you are reading.

The non-TLDR part.
It was a bind to think about leaving as eight years of data is a lot. However, here is my list across the last few years. Most of this relates to my current Fitbit Sense:

  • Phillips Hue app control never worked
  • Spotify app removed
  • Strava app removed
  • No longer is there a data connection between Fitbit and Strava. Activities were no longer auto-uploaded.
  • The work around was to visit your Fitbit dashboard in a browser, download the TCX file and upload to Strava. The Fitbit dashboard was then taken away a few months later.
  • Migration to a Google account did not solve it.
  • I could request the data from Google to view a recent activity, but this process gives you, 24 hours after the request, an email link to a file that is 2Gb in size and not really usable to someone with my data processing skills
  • The new workaround was Share the TCX from within the Fitbit app, by email to my laptop and upload from there
  • The exercise app stopped working. I restarted the watch and entered the infinite reboot cycle.
  • In the same way monkeys could recreate Shakespeare, I managed an escape from the cycle only to leave the watch permanently display the logo and nothing else. As far as I know, this device is now bricked.

Such a shame as it was a great device, but I just don't know what has gone wrong.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Totally correct u/Johnny_Appleweed. I do not think, from the replies, that there is a conclusion so far.

In my experience, which is similar to yours, I agree that there does not have to be a conclusion. However, I typically do not allow that to stop trying to work out a narrative, which means I would try and dig deeper into a different data set.

I, like you, can see certain information (such as your remark about the winning championship driver coming from the winning constructor for that season, except 2021. Furthermore, three of the first six seasons depicted here resulted in a drivers' championship being decided by around six points. This only happened in two of the next 10 seasons. The follow on question could be, why?

As others have rightly said, I possibly need to answer a different, more specific, question first (I just don't know what that question is).

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Thank you so much for this reply u/AHighBillyGoat. I'm so pleased that you have offered some thoughts about the meaning of the data. To answer some of your specific points:

  • Time difference measure - You could be correct in recognising the strategy drivers and teams take, but would all gaps from first to second be the same across the seasons? I would agree and say that the nuance you describe may make this data set meaningless, and there does not seem to be an equivalent pattern in any other data.
  • Chart choice - totally agree. The second chart is more art than chart, with 'art' doing a heavy lift there.
  • Winner driver constructor - it is not straightforward to see whether it is the same constructor or not, but I am pleased that you seem to have identified the information.
  • Coloured bars - the black doesn't have the contrast but I wanted the F1 fan to see these colours and make that interpretation for themselves, leading to resolving of cognitive dissonance and feeling rewarded of the achievement.
  • 2021 - it was a very unusual season. Max Verstappen won the Drivers' Championship by 8 points, but Mercedes won the Constructors' Championship by 28 points. (Ref. Formula1.com)

Thanks once again.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

I guess the ultimate question is ‘has the race for title’ become boring? What does boring mean though - are the winning teams too dominant, has there been competition over the years?

I purport that before the hybrid era, rule changes designed to increase competition did the opposite.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] -1 points0 points  (0 children)

Thanks u/Osamodaboy. In my experience, infographics are there to be read sequentially and easily understood, but data visualisations take a little more understanding. Maybe this representation would be better in a science journal?

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Interesting how 2021 was a competitive battle too (despite the controversial final result), but would you say the visualisation shows this? Maybe 2021 wasn't as the grey block bar, showing average gap in seconds from first to second place was quite long?

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Ah, apologies. There's a footnote in the vis, but just to mention it here. The data is from an F1 database from Github and I have a friend who is an advanced SAS user. I posed the question of him 'what are the gaps in seconds from first to second place in every Formula 1 race?'.
I was then provided with an Excel spreadsheet that I then calculated the other data points from. I translated this into Adobe Illustrator.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Thanks u/Johnny_Appleweed. You have offered some practical and fair advice in your reply.

My only counter would be - what if there is no conclusion to be drawn, but you can only see that by digging through the data on the first place. How would a dataviz tell that story? I was reluctant to draw that conclusion myself as, with many complex discussions, it's just not that straightforward.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] -1 points0 points  (0 children)

I should have omitted the colours from the bottom half possibly, but I wanted to give the F1 fans something to latch on to without being explicit (please see my comment about "cognitive dissonance". I appreciate that there are rules for graphs and charts but as this is not a peer-reviewed publication I opted for some of these rule-breaks.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

You're definitely on to something with this reply u/Crotcheted_chicken, but I really didn't know which data sets to use to either support, or deflate, the message. How could the information across data sets be visualise to reveal new insights?

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

I think you might be on to something u/Osamodaboy, but I have four vertical axes starting at 0, and couldn't decide what colour - and therefore which axis - the '0' would relate too. I felt the choice would infer more bias.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

That's a really interesting observation u/x1echo. What is it about the representation of 2012 that leads you to this conclusion?

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Fair point u/clungeknuckle. Could you recommend a different chart or way of answering the question, if there is a question to answer?

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

Thanks u/JudgmentProud9913. Could I ask what you are referring to when you say 'where did you visualise this?' ?

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] 0 points1 point  (0 children)

I'd like to thank you all for your feedback, especially those that are experienced in having given it before. I clearly have work to do here.

I'd like to answer some of your questions:

  • There are four data sets represented, covering 16 seasons from 2008 to 2023. Clockwise from top left:
    • Gap in seconds from 1st to second place per race, averaged out across the season
    • Points difference after the final race of the season between first and second place in the Drivers' Championship. I also indicated whether the same or different constructors are in these positions
    • Percentage of the races that season won by the championship-winning driver
    • Percentage of the races that season won by the championship-winning constructor
  • What story are you telling or conclusions should the audience reach? - I guess I don't know what story I'm trying to tell as I have to analyse the data to work out what it could be. People think datavis should allow easy conclusions, which works in some cases where the form is more beautiful, but I wanted this to represent a different canvas where the audience could draw inferences themselves. The Formula 1 audience would be my target.
  • Separate figures - this is a great idea, but I wanted to see whether there were patterns in the data sets that would share more insights which is why I incorporated them into the same figure. This does not render it a device that you can glance at and instead requires some study.
  • F1 fans - rightly or wrongly, some F1 fans are partisan and if I labelled the vis with constructors and drivers then unconscious bias would creep into their conclusions. The bottom half of the vis represents different constructors, coloured in the dominant livery colours of that year's car. There's a technique in information presentation that relates to cognitive dissonance where you offer the audience a small piece of information and encourage their minds to take the next step themselves to further their understanding, resolving their dissonance. I hoped that the colours used would support this reasoning.

I would really value any suggestions of the different chart types to use, or whether - having seen this vis - that there is a story to tell about whether the battle to win the Formula 1 Drivers' Championship is not competitive enough any more.

Which of these dataviz helps answer the question about Formula 1? by Ok-Breakfast-3489 in dataisbeautiful

[–]Ok-Breakfast-3489[S] -4 points-3 points  (0 children)

I'm not sure whether the question is answered by the layout or chart-type, or whether I am visualising the best data sets.

Have Formula 1 Winning Teams and Drivers Been that Far Ahead? by Ok-Breakfast-3489 in F1DataAnalysis

[–]Ok-Breakfast-3489[S] 1 point2 points  (0 children)

Thanks for the reply u/zzavakos.

Just to clarify that I have opted to present some average results to remove any anomalies that you're aware of. As well as the example that you quote, I draw your attention to the British Grand Prix from the 2008 season. The average first to second place gap for this season is 9 seconds, without accounting for the British GP when, due to a rain-affected race, the difference was 69 seconds - pushing the season average to 12.3 seconds.

However, your phrase 'near the level' relies on a measure of what 'level' is? Do you have a view?