Data is a wonderful thing, isn’t it…?! Take the following image, for example:
It depicts the racetrack used for the Bahrain Formula One grand prix last week, and it was generated from 2 laps worth of data collected at 1 second intervals from Lewis Hamilton’s car.
Long time readers will probably know that over the last couple of years I tried to track down bits of F1 motor racing related data, but to no real avail. Last year, I had the chance to look round the Red Bull Factory a couple of times (it’s located just a few minutes walk away from the OU campus in Milton Keynes) and chat to a couple of the folk there about possible outreach activites around data among other things (I’m still hopeful that something may come of that…).
Related to that possibility, we commissioned a rather nice gadget for displaying time series data against a map, in anticipation of getting car data we could visualise.
Anyway, ever impatient, when I saw that the Mclaren F1 website included a racetime dashboard, a Dev8D payoff in the form of a quick twitter conversation with @bencc resulted in him grabbing the last 30 or so laps worth of that data… :-)
I’ve had a quick play with it to see what sorts of thing might be possible (such as the map above), and there’s a whole bundle of stories that I think the data can turn up. I intend to explore as many of these stories as I can over the next few months, hopefully aided, abetted and corrected by a colleague from my department who is far better at physics than me… because the data contains a whole raft of physics related stories (and remember, folks: physics is fun).
If this: a) sounds like a turn off, but b) you claim to be interested in things like data journalism, please try to stick with the posts in this series (who knows – it may even turn into an uncourse;-) On the other hand, if you’re an F1 junkie (i.e. you follow @sidepodcast and/or listen to the Sidepodcast podcast;-) I’ll tag the posts f1data, so you can visit the tag page or grab the feed if you want to and not have to expose yourself to the rest of the ramblings that appear on this blog…
There’ll be no magic involved, though the results may turn out to be magical if you’re into geeky F1 stuff…;-) but to try and widen the appeal I’ll try to explore what stories the data holds about what’s happening to the car and driver, reflect a bit on what we can learn about extracting stories from data, and look to try to unpick the additional knowledge we might need to bring to the data in order to extract the most meaning from it; you can then see if these lessons apply to data – and stories – that you are interested in!
PS the F1 Fanatic blog is also doing a series on visualising and making sense of F1 related data, as this post on Bahrain Grand Prix FP2 analysis demonstrates. (See my own attempts at doing similar things with timing related data from last year: Visualising Lap Time Data – Australian Grand Prix, 2009). [UPDATE: As pointed out in the comments – and how could I have forgotten this?! (doh!) – there is also the F1 Numbers blog.]
Depending on how my F1 data related posts go, I may even try to hook up with the F1 Fanatic blog and/or the Sidepodcast folk to see if we can work together on ways of presenting this data stuff in a way that your everyday F1 fan might appreciate; just like T151 Digital Worlds helps your everyday computer game player appreciate just what’s involved in the design, development, business and culture of computer gaming:-) <- that’s a shameless plug if Christine or Mr C are reading…:-)
5 thoughts on “F1 Data Junkie – Getting Started”
message received ;)
not sure if there’s anything we can do, but would love to be involved somehow. a quick plug also for gavin, who does a really god job of presenting accessible f1 data too: http://f1numbers.wordpress.com/
Thanks for that reminder – how could I have forgotten that (slow start to the year for me!)
I found your blog because of Mr. C.’s link above and find the #f1data posts very interesting. Would you like some assistance or collaboration here?
As you have said, F1 data can be quite tricky to find on the web, but I think you may have found a nice little gold mine here.
Have a look at my blog and if you think we could work on something together then please feel free to contact me. My degree is a Bachelor of Science in Mathematics, but I also did some physics and in my day job as a Data Analyst, use a lot of statistics too.
Gavin Brown (RubberGoat)
Apols for not mentioning F1 Numbers first time round – I discovered late on last year, but then took the close season off following F1 online. As to working on something together, I think that would be a great idea. Maybe a place to start would be to agree some sort of tagging convention, so we can pull content together in some sort of dashboard view around particular areas (e.g. http://ouseful.open.ac.uk/blogarchive/013966.html ). I don’t know if Keith at F1 Fanatic would be interested in this approach too? We could maybe also discuss exploring either particular themes at the same time to see if we have a different take on it, or complementing each others sites by informally sharing out who’s going to look at what, based on each of our interests?
One of the approaches in this series of posts I’m keen to explore is my attempts at making sense of the data and looking for stories in it that are then expanded on, corrected maybe, extended and refined by “someone who knows”…This boils down to me trying to learn how to do things in public, which is the flip side to the traditional teacher role, but one I think that may be powerful in an informal learning context (apols for going off the F1 topic – this blog has ed tech readers too!;-)
A couple of years ago I tried writing the stub of an OU course in public on a blog. Although I didn’t know specifically where the course was going to go when I started it, I did try to scaffold it around a set of topics I thought I wanted to explore that I popped onto a simple mindmap. Maybe between us we could create a skeleton map or outline of areas we think can tell data stories around and then write around that as the opportunity takes us? Your work as a data analyst (what sector, out of interest?) could possibly bring in all sorts of related anecdotes about using similar techniques elsewhere to help folk realise that data is all around us and can be put to work if you just know how…
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