I have been a fan of sport since I was a little boy standing on a milk crate on the terraces at Portman Road (when I was a bit older a family friend made me a wooden box he had painted blue and white to stand on – writing this makes me feel very old).
So when I saw that Spencer Baucke, Simon Beaumont and James Smith had created an initiative called SportsVizSunday I was keen to throw my hat in the ring. As well as encouraging people to share sporting vizzes on data.world and twitter (using hashtag #SportsVizSunday), the team also supply a dataset each month with a sporting theme.
In this post I will showcase my contributions to the project, with a design or technical tip thrown in for good measure. For a perspective on why sport is such a good topic for visualisations, I highly recommend this blog post by Neil Richards.
March – Formula One
To be honest, I find Formula One intensely dull (unfortunately it is the only sport my partner watches), but Simon gave me a nudge at the Midlands Tableau User Group so I thought I would give it a go!
My viz focuses on individual race winners, highlighting a select band of drivers who have dominated different eras:
Viz tip – I wanted to provide a way of users to quickly highlight winning drivers that weren’t in this select band. Here are the steps I took to do this (I have used variations of this in a number of vizzes and it is a great way of making your viz configurable).
- Created a parameter ‘Select driver’ using the field containing winning drivers (allowable values = list, add from field)
- Created a calculated field ‘Selected driver’ with a value of 1 for the driver selected using the parameter dropdown (IF [full name] = [Select driver] THEN 1 ELSE 0 END)
- Included the value for ‘Selected driver’ in the calculated fields for colour and size, so the selected driver is represented by a large, orange dot.
April – Masters Golf
Golf is another sport I don’t know much about, but I had seen Rob Radburn’s viz and had been intrigued by just how many tournaments some players had been involved in (50 or more for Gary Player and Arnold Palmer).
I hadn’t known that previous winners are invited back to play every year, so my viz focuses on this aspect and on how their performance deteriorates over the years (Jack Nicklaus is an interesting outlier here).
Viz tip – I took great care over the placement of the player names in this viz. Here is the default view, with the player name as a row header:
It looks alright for the early years of the tournament, but the amount of space between the name and the marks for the more recent winners make them hard to identify. Here is how it looks as a left-aligned label:
The labels are nearer the values, but they are all over the place as my data is ordered by the year of the first win, not the first tournament, for each player. So in my final viz I adjusted this so that no label is further to the right than any one above it (I did this by manually adding a column to the dataset, although it may be possible using a calculated field).
A small decision, but I think it makes a big difference.
April – World Championship Snooker
My final viz is on snooker, one of my favourite spectator sports. For British sport fans of a certain age the 1985 World Snooker final was an iconic moment (I remember being allowed up past midnight to watch the end of the match, which was decided by the final pot of the game after almost 15 hours of snooker).
Neil Richards created a trademark radial viz on the same game, which you can see here.
Viz tip – How to visualise each shot in the final frame was an interesting challenge. I knew I wanted the balls potted to be represented as intuitively as possible using coloured circles:
However I wanted to make it more obvious where a player had made a break. The answer? A dual axis chart, with a (very thin) Gantt chart representing break duration. Note that where Davis fouled (black diamond) then was put back in by Taylor and played a safety (light grey diamond), I purposely did not connect the two shots:
And the snooker table plots? I could have created these as images, but to allow me to alter the ball positions I created these as scatter plots, with the snooker table as a background image.