Why is sport such a good topic for visualisations?

Sport visualisations won’t be for everyone, but I do think that the debate on this question is quite simple. Sports by their nature are full of numbers and data: times, goals, scores, dates, rankings, statistics, averages, records, tables, and the like. Most of these are not too difficult to get hold of, either via Wikipedia or more specialist websites, and the datasets are often therefore complete and in good shape. None (or not too much) of the pesky data cleaning so often needed.

And also, lots of us enjoy following sport. When learning, practising or just visualising for fun, there’s nothing better than visualising data which you enjoy or that you can recognise or relate to. So whereas this second factor will rule out sports visualisations for many (those for which sport doesn’t hold the same interest), for those that doesn’t then it’s one of my first bits of advice for those new to data visualisation – when choosing data, sometimes you might be put off by company data: sales, profits and the like, but you can understand better if you are working with data you enjoy. And if that’s sports data, then dive in!

So I’m going to lay myself bare here with the first visualisation I ever published on Tableau Public:

 

I won’t go into too much detail here, as I think my experience and skill level has improved since then (I shudder at my use of a bubble chart, for example). This was very much an exercise in understanding what viz types were available, how to use them, some of the available formatting options and other such like. But, after looking at cricket data, I understood it so much more. I created the dataset (albeit by copy/paste from websites), I knew what results to expect (having followed the cricket series recently) and I was able to find some insight around batting and bowling averages, which made sense to me.

My second visualisation was how I managed to understand using line charts and cumulative totals, using times relative to the minimum (i.e. the leader). This was the Tour de France in 2015. I’d been following the Tour and knew what results to expect, and so was delighted with the results.

By this point, I knew that I would often turn to sports data for personal projects or to learn more skills.

Fast forward almost three years and I still love to create sports visualisations. From a month or so ago, here’s my US Masters Golf visualisation

I enjoyed creating this for lots of reasons, but three reasons stood out:

• It’s a data visualisation, it’s another look at timelines and comparisons of careers, I love this kind of stuff
• It was part of a group effort. As I was doing this, so were tens of others of visualisation and sports enthusiasts. I was really looking forward to seeing what else would be created
• I like my golf, and as I’m not a young man, I saw and recognised a lot of trends, information and data that I was familiar with. But I found some interesting results, some actual analysis. The difference in round performances between two of the greats: Nicklaus and Woods, is really emphasised in a way that you might not have known (I certainly didn’t) without visualising.

Which brings me to Sports Viz Sunday – an initiative conceived and run by Simon Beaumont, James Smith and Spencer Baucke

If you like to create sports visualisations, whatever day of the week, post them here, simple! And, unsurprisingly, there’s the #SportsVizSunday hashtag too. The site is active throughout the year, but in addition: once a month, there’ll be a dataset posted around a different major sporting event which will lead to a challenge to visualise the dataset alongside your co-participants. The Masters Golf led to a wide range of visualisations, as showcased here:

#SportsVizSunday #TheMasters April 18 data viz challenge

There have been monthly datasets on Formula One, Masters Golf, Winter Olympics and more to come (in fact yours truly has been asked to select a dataset for May). This is also a great chance for me to showcase some of the great visualisations already created. There are far too many for me to include all the great work, but I’ve included three of the many that caught my eye for great design and insight below:

1. From Justin Davis – charting the debut season of the Vegas Golden Knights

2. From Rodrigo Calloni – great visualisation of World Cup wins and losses

3. From Simon Beaumont – a load of balls showing US PGA Tour player performance

There is no judging, no rules or requirements, but it’s a great pooled community resource to flex your sport visualisation muscles (and it’s not Tableau-specific).

I could cite many more examples, not least my own (yes, I couldn’t resist another Lexis chart, this time on Tennis World Number ones …

… but it’s time to stop and publish. After all, tomorrow is Sunday, and it occurred to me that I’ve never seen a snooker visualisation before. Such a ripe source of numbers, data and colours, it’s about time that changed, but that will mean time to stop blogging and start visualising.