Along with David Krupp, Lindsey is probably the “vizzer” who inspires me most at the moment. Both deliver consistent, clean and informative designs which I can learn from and apply at work, and it’s an aspiration of mine to up my game to a level at least approaching a par with them.
I’ve previously looked at one of Lindsey’s dashboards, and a recent creation of hers prompted me to want to go through the rebuilding process again. In this instance, I just downloaded the workbook and walked through each worksheet step by step.
This is by far the most complicated sheet in the workbook. I have no idea what sport this is, so the Measures etc. mean absolutely nothing to me. What I can deduce is that we’re splitting the view up by Season and plotting wins within each season. Some cunning is employed to line up results of some sort along the same horizontal plane, and that can be confirmed by looking at the tooltips:
It transpires that this means that in 2006, Texas Tech, Kansas State, Iowa State and Oklahoma State all ended the season with a 6W 10L record. Where teams with identical records are plotted, they’re lined up along the same horizontal space. Let’s see how that’s done.
First thought? Oh crap! I know that the % does this:
Returns the remainder of a division operation. For example, 9 % 2 returns 1 because 2 goes into 9 four times with a remainder of 1. Modulo can only operate on integers.
That’s all well and good, but it’s a straight whoosh over my head. Time to tweak a view and add some labels:
The 1.0’s appear because, as an example, Kansas in 2007 were the only team with a 14-2 record. 1/1 leaves no remainder, so adding 1 yields 1.
So what if two teams have identical records? In 2005, Nebraska and Missouri had 7-9 records, and their [Ranking Alignment] is:
The [Ranking Alignment] computes like this:
The inclusion of Team is relevant, as it increments the INDEX(), which is running across the table:
So 2/2 leaves 0, plus 0.5 assigns 0.5 to the first team. 2/1 leaves 1, plus 0.5 assigns 1.5 to the second team. Phew.
This sheet does not end there! A nice feature of the sheet is that dependent upon the team you select, reference lines are drawn with (presumably) the team colours, to make their performance stand out. This was a labour of love for Lindsey (and also indicative of the effort that goes into separating vizzes of this quality from the field), as it required her to create 14 separate reference line calcs:
The logic is basic enough, and it’s another of those reassuring “touches” that is simple to implement but adds so much to the overall impact of a viz. Colour is determined via two components added to the colour shelf. Team is an obvious inclusion, but as Lindsey only wants to Colour the selected team, there’s a boolean calculation thrown into the mix too:
There are then a series of “Big Ass Numbers”, and I love these as they are becoming the bedrock of dashboards we create at work. If you want people to immediately grasp the key message of a piece of analysis, what better way than in-your-face numbers at the top of a viz? Generally, they are simple:
In one case, there was some manual work involved:
This was just to show the overall head to head record of teams against Kansas. It could possibly be calculated, but why cock about with all that if you can just take a shortcut? This is married with a further calculation to ensure that things “make sense” if Kansas is the selected team, as obviously Kansas can’t have a head to head record against themselves.
Next, we have a horizontal bar chart:
Not a huge amount to write home about here, but it does include a nice use of a Row axis ruler to create that white “baseline” on the chart. Again, it’s a subtle, incremental inclusion that adds to the viz as a whole:
Another example of design flourish in the final dashboard is the coloured bar under the main headers, which adjusts based on the team selected – how was that achieved? It’s just a bar chart, which is Floated in a small object and set to Fit Entire View. Again – simple and effective:
In essence, “that’s it”. There’s still obviously a lot of precise work involved in bringing a multitude of dashboard objects together to form a coherent whole, but by and large the core elements of the viz are quite basic. The use of the modulo operator takes some time to get your head round (me at least), and it’s impressive (again, for me at least) to see that Lindsey knew that this was the operator to use to get this aspect of the dashboard “just so”.