OUseful.Info, the blog…

Digging around looking for stats and data relating to the first Formula One Grand Prix of the new season, I came across some interesting looking technical info on the Race Car Engineering website (Formula 1 2009: Round 1 Australia tech data), as well as details of the staring weighs of the vehicles following qualification (post-qualifying car weights).

Assuming that the weight of the fuel is the post-qualifying weight of the car minus the minimum weight of the car, it should be possible to have a guess at when the teams are planning their first pit stop. So I started doodling a spreadsheet that could be used to try and work out fuel’n'pitting strategies (albeit very simplistically).

If you’re interested, you can find it here: Race Day Strategist Spreadsheet:

I’ve made a few assumptions about how to calculate how far the fuel will take a car, so if you can tell me if/where I’ve made any mistakes/errors/bad assumptions, please post a comment.

I’ve tried to make the working clear, where possible:

I also put together a ‘quick calculator’ that could be used to play-along-a-strategist while watching the race.

All the formulae were made up on the fly (“hmm, this could be interesting?”) so when I get a chance, I do a little reading to find out how other people have addressed the issue. (I’ve already found links for a couple of things I probably ought to reqad: Practice Work – Optimization of F1 – PIT STOP TACTICS (which may contain some interesting ideas) and the rather more involved Planning Formula One race strategies using discrete-event simulation (subscription required – so OU folks should be okay through the OU library. If there are any other things you think I should add to the list, please pop a reference to them in the comments.)

This spreadsheet could obviously go much further – addressing other pit stop timing delays, tyre considerations etc. Being able to pull in live timing data – e.g. time intervals between the car of interest and other vehicles – and predict car lap times would also add a little more intrigue when trying to decide whether or not to pit.

But it’s a start, and it got me asking a few questions that might not otherwise have come to mind ;-)

All I need to do now is work in the visual angle, maybe taking a little inspiration from Visualising Lap Time Data – Australian Grand Prix, 2009…

One of the, err, side projects I’ve been looking at with a couple of people from the OBU has been bouncing around a few ideas about how we might “wrap” coverage of Formula One races with some open educational resources.

So with the first race of the new season over, I thought I’d have a quick play with some of the results data…

First off, where to get the results info? An API source doesn’t seem to be available anywhere that I’ve found as a free service, but the FIA media centre do publish a lot of the data (albeit in a PDF format): F1 Media Centre – Melbourne Grand Prix, 2009.

For convenience as much as anything, I thought I’d use Many Eyes Wikified to produce a set of visualisations based on the lap time data and the race lap chart.

To get the data into an appropriate form required a little bit of processing (for example, recasting the race lap chart to provide the ranking per lap ordered by driver) but as ever, most of the charts fell out easily enough (although a couple more issues were raised – like being able to specify the minimum y-axis range value on a bar chart, for example).

Anyway, you can find the charts linked to from here: Australia Lap Times visualisation.

In the meantime, here are some examples (click through to reach the interactive original).

First up, a scatter plot to compare lap times for each driver across the race:

Secondly, a line chart to compare time series lap times across different drivers:

This bar chart views lets you compare the lap times for each driver over a subset of laps:

A “traditional” drivers standings chart for each lap:

Finally, this bar chart can be run as an animation (sort of) to show the rank of each driver for each lap during the race:

There are a few more data sets (e.g. pitting behaviour) that I haven’t had a look at yet, but if and when I do, I will link to them from the Australia Lap Times visualisation page on Many Eyes Wikified.

PS If you’re really into thinking about the data, maybe you’d like to help me think around how to improve the “Pit stop strategist” spreadsheet I started messing around with too?! ;-)

PPS It’s now time for the 2010 season, and this year, there’s some Mclaren car telemetry data to play with. For example, here’s a video preview of my interactive Mclaren data explorer.