Hello, hello to one and all.
I'm writing this week's round-up while watching the BBC pundits discussing some of the themes surrounding the 2022 Men's Football World Cup. While I am intending to support England's progress through the tournament, I am - like many others - somewhat conflicted as I believe that sport should be a welcoming environment for all participants and spectators. It will be interesting to see how the conversations continue as the tournament progresses.
Turning now to some sporting visualisations, unsurprisingly I'll start with a few inspired by the World Cup. First up is Bo McCready's visualisation showing where each player in each squad plays their club football. It's fascinating to see the different relationships between nations and where their players actually play. The use of flags as icons makes it really easy to spot the nations with a lot of domestic representation.
As a little bit of bonus knowledge, the three Ecuadorian players who play in England also all play for the same Premier League club - Brighton & Hove Albion!
Next is Chris Westlake's circles/growth-rings representation of how well each nation has performed in previous World Cups. I could look at this for ages, and I think his use of bright yellow to anchor one end of the colour scale really draws your eye in. I found myself contrasting the conflicting fortunes of Uruguay, Belgium and the Netherlands. I was surprised to see that Germany looked the most successful too - I would have guessed Brazil if you had asked me, but that doesn't appear to be the case (visually at least!).
As an aside, I also found myself wondering how each nation would sound if this was converted into an audio scale too.
For anyone who would like to know more about each nation in the World Cup, underperforming my expected beers has put together an exhaustive dashboard to tell the story of each nation's performance over the last four years. There is an absolute wealth of information in here, both about how each nation plays as a whole, and all the individuals who have featured. The goals for and against by match time was something that stood out to me, and I recommend having a deep dive into your favourite nation and seeing what new perspectives you can find.
Ryan Soares has looked at the ages of each nation's players in this very clean and elegant visualisation. I didn't realise that Brazil, Belgium and Uruguay had such old squads. I wonder whether the extra experience will pay off for them, or if they will find themselves surprised by the energy of some of the younger squads.
Moving away from the football, Karl Ericson has updated our April challenge dataset to look at the winners of the Indianapolis 500. I really like the design and layout of this visualisation, and it is packed with information. I don't know much about the Indy 500, and I particularly enjoyed looking through the tooltip detail of all the individual winners. Also, Team Penske clearly know which end of the car is the front!
Staying with four wheeled racing, motor_racing_addict has updated their very comprehensive dashboard tracking Formula 1. In particular, they've highlighted some tight battles for second place in both the driver's and constructor's championships and I'd recommend looking through the whole dashboard if you're a Formula 1 fan.
One of the things I like most about writing this blog is that I get to learn about what is happening in sports that I don't follow so closely - in this case, ice skating. Ausrine has created a wonderful visualisation based on the Figure Skating Grand Prix 2022-23, where the top 6 skaters in each discipline will skate in the Grand Prix Final in Torino. I like the simple design throughout this visualisation, and the choice of graphs for each section really suits the data too.
What really struck me though was Ausrine's closing analysis about how skaters could have achieved more ranking points had they been assigned to compete in different Grands Prix. I'm always interested in how different sports handle any sort of qualification, and how the choices that are made about that process can directly influence who qualifies.
Kevin Flerlage has created this beautiful dashboard to show off Connor High School Boy's bowling stats to their very best. I like every part of this dashboard and I thoroughly enjoyed spending time looking across all the graphs to build a narrative of which bowlers were best and why. In particular, I want to draw attention to the Individual Average and Range of Scores which to me are a masterpiece of elegant design and impactful information.
CJ and myself were talking about this great visualisation from Tanya Shapiro showing the passing yards for NFL quarterbacks, and particularly whether we could build the boxplot/icon mashup in Tableau because it is really effective! (we think we can, so maybe watch this space - or have a go if you think you can too!)
More generally, Todd Whitehead, who has been featured in our round-ups before, has put together a list of sports data viz people that he has been admiring recently. Definitely worth a look through for some inspiration, there's some wonderful things being created.
Lastly, don't forget that this month's challenge is Kate's farewell looking at her favourite baseball pitcher, Pedro Martinez. As always, you can use our data on its own to create some cool visualisations or you can combine it with your own too!
Farewell until next week!
Mo & the #SportsVizSunday team