#He’s a pinball wizard there has to be a twist. A pinball wizard, got such a supple wrist.. do do de do… How do you think he does it? What makes him so goood?#
Good morning all. I don’t start every post with a song but at the moment I can’t get that tune out of my head. And it’s no surprise given the subject of the post.
Peter Gilks recent viz about video games consoles got me reminiscing back to the heady days of 8-bit gaming and all those days I spent in dodgy arcades in the North-East of England. And I got thinking about all the great pinball tables I used to play, both back then and later at University. So as with most thoughts us BI folk tend to have, it gravitated into a Tableau viz!
And here it is.
A viz of all pinball tables where more than 1000 units were manufactured. The timeline shows release date, coloured by manufacturer. I’ve also pulled a table rating and the exact number of units produced.
So how did I do this?
Data is obviously the key to any viz. And luckily the folks at The Internet Pinball Database have a detailed repository, easy to query and returns the results in a format that I can easily get into Excel. So step 1 was to use their search functionality and pull out a list of all tables with >1000 units produced and copy that into Excel. Simple enough. I was hoping to locate a really detailed datasource and given the fact loads of people take their flippering very seriously it wasn’t a surprise to find this resource.
2. Viz design
Now I knew from experience that pinball had a “golden age”, was hit by the advent of video games and then made a bit of a comeback. But would that be reflected in the data? So my first view was a timeline by year, coloured by manufacturer to show the big players like Williams and Gottlieb etc.
That was simple enough to create but I wanted to give it a kind of 8-bit graphics feel, like you’d get on a table display, and that was done with a stacked bar chart, carefully choosing the colours and border around each cell. I really like the look of it, especially from a distance. Works great when using the manufacturer highlight also.
The other 2 views were self explanatory enough, highlighting the ratings and units produced. I had planned on limiting them to the top 20 tables so I didn’t have annoying scrollbars (I hate those things), but a couple of people I showed it to said they’d like to see more detail. Let me know what you think.
#Aint got no distractions. Can’t hear those buzzers and bells. Don’t see lights a flashing. Plays by sense of smell. Always gets a replay, never seen him faaalll… # Sorry – there it goes again… Be gone Daltrey!
I wanted to highlight and filter according to manufacturer. Highlights look great on the timeline with all those little squares lit up like a video game, but it would also be nice to filter the whole sheet by manufacturer to tell their own story through the years and view their top / most produced tables. This was achieved by allowing the tables by manufacturer chart to double up as a dashboard filter.
I also added a filter for machine types, so you can go back the really old-school days of Electro-Mechanical tables, before they all became Solid-State.
4. Graphical Grief
I really wanted to get some more graphics into the viz, pictures of tables etc, but ran into two issues.
Firstly I did manage to find all the images on ipdb.org, but there were far too many to use as custom shapes or icons. So I figured that I’d be able to have a web page object that linked back to the image at source, displaying the table whenever it was selected by the user. That would be ace.
All I needed was the URL path for one image for each table. Now I’m sure as hell not gonna manually copy the URLs into the datasource so managed to achieve this with the help of a colleague (@GlenRobinson72) who knocked up this VBScript.
It works by taking a text file of pinball table names, and for each one it connects to ipdb.org, searches for the table, finds an image file and parses the URL, which it returns back to me. I then manually copy that into the datasource csv as a dimension. Dead easy. It doesn’t always get the best image for each table but that’s less of a concern.
Then the next step was to create a dashboard URL action, that referenced the new photo URL dimension.
And finally a web page object that dynamically changed URL destination based on what table was selected.
That works great. BUT – the web page object has no control over the size of the image returned. I’d wanted each image to resize into the web page object but what ended up happening was that only a tiny portion of the image was displayed with huge scrollbars. I was unable to find a workaround to this.
For example, see the image in the bottom right corner of the screenshot (left).
I did find a compromise. I removed the line in the script that stripped the “tn_” from the URL and this meant that the thumbnail image was returned. Too small to be of real use but more workable than the large image.
If anyone knows a way of getting around this then let me know. It would have been the icing on the cake for this viz. I suppose I could have downloaded the images locally, resized them manually and then used them. I wonder if Alteryx could be used to solve this problem?
5. If I’d had more time
I managed to find a data set of world’s best pinball players and their ranking points totals from 2013. That would have been good to represent, and maybe correlate with their favourite tables. The data is at http://www.ifpapinball.com/ if anyone is interested.
I also wanted to represent the World Record score for each table, but wasn’t able to find that data.
The Finished Product
I’m well pleased with this. In particular the way the colour matrix works for the timeline chart. Looks funky.
And there are a surprising number of trends to infer from the data. You can see the golden age of pinball from the mid 60’s to late 70’s, when the impact of Space Invaders and Pac-Man led to a crash in table production. And then the mini-revival in the early 90’s, which is when I ploughed loads of my student grant into them. Stern are still producing tables to the present day but these are in lower numbers than 1000 so are not included in this dataset.
Looking at ratings you can see Gottlieb have by far the biggest presence at the top of the ratings chart, even though they didn’t produce individual tables in huge numbers. But they clearly have a cult following as the ratings suggest. Williams and Bally seemed to go for large numbers, consistently across the golden age, with Williams continuing into the 90’s revival. Of course none of that would have been possible without the early pioneers like Chicago Coin and Genco.
No surprise to see The Addams Family as the top produced table. That thing was everywhere. I never got on with the magnetic madness on that table although everyone else seemed to love it. I’ve called out my favourite table in an annotation. As far as I’m concerned it was directly responsible for me not getting a 1st in my undergraduate degree so has a lot to answer for.
So there it is. Hope you like it. Feedback invited as always. And don’t buy that stuff about the deaf dumb and blind kid. I’ve tried playing blindfolded….
Regards, Tommy – agh sorry!