Tata F1 Connectivity Innovation Prize, 2015 – Mood Board Notes

It’s probably the wrong way round to do this – I’ve already done an original quick sketch, after all – but I thought I’d put together a mood board collection of images and design ideas relating to the 2015 Tata F1 Connectivity Innovation Prize to see what else is current in the world of motorsport telemetry display design just in case I do get round to entering the competition and need to bulk up the entry a bit with additional qualification…

First up, some imagery from last year’s challenge brief – and F1 timing screen; note the black background the use of a particular colour palette:

In the following images, click through on the image to see the original link.

How about some context – what sort of setting might the displays be used in?

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From flatvision case study of the Lotus F1 pit wall basic requirements include:

  • sunlight readable displays to be integrated within a mobile pit wall;
  • a display bright enough to be viewed in all light conditions.

The solution included ‘9 x 24” Transflective sunlight readable monitors, featuring a 1920×1200 resolution’. SO that fives some idea of real estate available per screen.

So how about some example displays…

The following seems to come from a telemetry dash product:

telemetry_main_screen

There’s a lot of text on that display, and what also looks like timing screen info about other cars. The rev counter uses bar segments that increase in size (something I’ve seen on a lot of other car dashboards). The numerbs are big and bold, with units identifying what the value relates to.

The following chart (the engineer’s view from something called The Pitwall Project) provides an indication of tyre status in the left hand column, with steering and pedal indicators (i.e. driver actions) in the right hand column.

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Here’s a view (from an unknown source) that also displays separate tyre data:

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Another take on displaying the wheel layout and a partial gauge view in the right hand column:

GAUGE track

Or how about relating tyre indicator values even more closely to the host vehicle?

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This Race Technology Monitor screen uses a segmented bar for the majority of numerical displays. These display types give a quantised display, compared to the continuously varying numerical indicator. The display also shows historical traces, presumably of the corresponding quantity?

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The following dashes show a dial rich view compared to a more numerical display:

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The following sample dash seems to be a recreation for a simulation game? Note the spectrum coloured bar that has a full range outline, and the use of colour in the block colour background indicators. Note also the combined bar and label views (the label in the mid-point of the bar – which means is may have to straddle two differently coloured backgrounds.

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The following Sim Racing Data Analysis display uses markers on the bars to identify notable values – max and min, perhaps? Or max and optimal?

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It also seems like there are a few mobile apps out there doing dashboard style displays – this one looks quite clean to me and demonstrates a range of colour and border styles:

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Here’s another app – and a dial heavy display style:

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Finally, some views on how to capture the history of the time series. The first one is health monitoring data – as you;d expect from health-monitoring related displays, it’s really clean looking:

FIA-seeking-real-time-human-telemetry-for-F1

 

I’m guessing the time-based trace goes left to right, but for our purposes, streaming the history from right to left, with the numerical indicator essentially bleeding into the line chart display, could work?

This view shows a crude way of putting several scales onto one line chart area:

telemetory

 

This curiosity is from one of my earlier experiments – “driver DNA”. For each of the four bands, lap count is on the vertical axis, distance round lap on the horizontal axis. The colour is the indicator value. The advantage of this is that you see patterns lap on lap, but the resolution of the most current value in a realtime trace might be hard to see?

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Some time ago, The Still design agency posted a concept pitwall for McLaren Mercedes. The images are still lurking in the Google Image search cache, and include example widgets:

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and an example tyre health monitor display:

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To my eye, those numbers are too far apart (the display is too wide and likely occluded by the animated line charts), and the semantics of the streaming are unclear (if the stream flows from the number, new numbers will come in at the left for the left hand tyres and from the right for the right hand ones?

And finally, an example of a typical post hoc race data capture analysis display/replay.

maxresdefault (1)Where do you start to look?!

PS in terms of implementation, a widget display seems sensible. Something like freeboard looks like it could provide a handy prototyping tool, or something like the RShiny dashboard backed up by JSON streaming support from jsonlite and HTML widgets wrapped by htmlwidgets.

Detecting Features in Data Using Symbolic Coding and Regular Expression Pattern Matching

One of the reasons I dive into motorsport results and timing data every so often is that it gives me a quite limited set of data to play with. In turn, this means I have to get creative when it comes to reshaping the data to see what visuals I can pull out of it, as generating derived datasets to see what other story forms and insights might be hidden in there.

One of the things I hope to do with the WRC data is push a bit more on automatically generating text-based race reports from the data. Part of the trick here is spotting patterns that can be be mapped onto textual tropes, common sorts of phrase or sentence that you are likely to see in the more vanilla forms of sports reporting. (“X led the race from the start”, “Despite a poor start to the stage, Y went on to win it, N seconds ahead of Z in second place” and so on.)

So how can we spot the patterns? One way is to write a SQL query that detects a particular pattern in the data and uses that to flag a possible event (for example, Detecting Undercuts in F1 Races Using R). Another might be to cast the data as a graph and then detect features using graph based algorithms (eg Identifying Position Change Groupings in Rank Ordered Lists).

During the middle of last night, I woke up wondering whether or not it would be possible to cast simple feature components as symbols and then use a regular expression pattern matcher to identify a particular sort of pattern from a symbolic string. So here’s a quick proof of concept…

From the WRC Monte Carlo 2107 rally, stage 3, some split times and rank positions at each split.

wrc_results_scraper10

Here’s a visual representation of the same (the number labels are rank position at each split, the y-axis is the delta to the fastest time recorded over that split (the “sector time”, if you will, derived data from the original results data).

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For each driver, you may be able to spot several shapes. For example, Ogier is way behind at the first split, but then gains over the rest of the stage, Kreeke and Breen lose time at the second split, Hanninen loses it on the final part of the stage, and so on. Can we code for these different patterns, and then detect them?

wrc_results_scraper11

So that seems to work okay… Now all I need to do is come up with some suitable symbolic encodings and pattern matching strings…

Hmmm… Vague memories… I wonder if there are any symbolic dynamics algorithms or finite state machine grammar parsers I could make use of?