Hello fellow #SportsVizSunday-ers.
As I write this, Liverpool and Man City have just drawn 2-2 in a very exciting game of football, (which does absolutely nothing to change the order at the top of the Premier League) and the final round of the Masters is beginning to tee-off with Scottie Sheffler sitting on top. I wonder if both Sheffler and Man City are just starting to look over their shoulders as they enter the final stretch with victory in their own hands, or whether they are entirely confident that their skills can take them over the line having got them this far.
On the subject of skills, the abundantly-talented Ashleigh Barty has recently retired from professional tennis at the relatively junior age of 25. Priyanka has created a visualisation to give us a quick recap of Barty's stats on different court surfaces. I admire the way Priyanka has kept it simple, which really focusses my eyes on just how many times Barty won!
Continuing on the tennis theme, Adam Green has created an absolute scorcher of a visualisation using our monthly challenge data from last month. Adam has used an area chart to show the flow of the Women's Singles Final from Wimbledon 2021 (which featured Ash Barty, so this match will be on Priyanka's visualisation somewhere too). I think he has balanced the colours and transparency well to be able to overlap both players' points so effectively.
There is so much to see and enjoy in Adam's visualisation, from the serving patterns through to the unforced errors. I was able to see how the first two sets had periods of relative calm whereas the final set was more tense. I highly recommend setting aside a few minutes to look at it, and follow how the match unfolded.
(as an aside, you can see all our previous monthly challenges here, and we actively encourage you to download the data and visualise anything that takes your fancy)
There have been a number of people making sports=related visualisations for the #30DayChartChallenge, and Vivek has created some very good-looking minimal maps of famous football stadiums for Day 7 using Python (and shared his code online too). I always find it interesting to look at the position of stadiums and arenas in cities, especially as they all tend to have a unique character.
Returning briefly to the Masters, Brett2point0 was looking into his crystal ball to try and predict a winner - and made this visualisation showing the official ranking through time of the current top 14 male golfers, particularly highlighting Sheffler's recent rise to the top. Brett felt that Cameron Smith was also looking good, and that prediction could yet come though given that Smith is on Sheffler's heels...
One of the best things about the #SportsVizSunday community is watching people continue to iterate and build on what other people have created. DB has taken inspiration from Yash (who has been featured by us before) and CJ (of our very own halls) to create this interactive Tableau dashboard showing the offensive pass map for any player in La Liga this season. It's a great technical feat and worth checking out.
Staying with football and technical wizardry, Halftime Heroes have combined Alteryx, Google Sheets and Tableau to create a very clean Premier League Round-Up Dashboard, showing all sorts of information about the teams in the Premier League. Even better, it is updated each day! I'll be checking back tomorrow to work out what the Liverpool vs City draw could mean for the title.
Jon Ollington regularly turns out insightful football visualisations, and this week he turned his eye to goal kicks in the Premier League. You can definitely see which teams like to play out from the back, and which teams are trying to play a more direct style further up the pitch. Man United, Leicester and Brentford's kicking distribution stood out to me too as being particularly left-side heavy which presumably reveals something about how they like to play that otherwise might not be obvious when you watch.
This dovetails nicely with Victor's analysis of how goalkeepers actually distribute the ball, using clipped or launched passes, and whether they are successful or not. There are some familiar names who are successful at both but some others who tend to shine with one or the other. There is a tactical chicken-and-egg here too - does a goalkeeper kick in a certain way because of the tactic of the team, or is the team set up in a certain way because the goalkeeper is better at one rather than the other?
Lastly, and somewhat awkwardly, is a visualisation that I created using this month's tennis data. I'm not going to describe my own work but I thought I might take the opportunity to explain a little bit about what I was going for and then leave it to you to decide whether you think I achieved it or not.
A tennis match is played between two sides who control half the court each. With this in mind, I wanted to experiment with how you could use that idea in a visualisation - showing the points moving from one side to the other - and if it would show you anything interesting about whether one side was more dominant than the other. I settled on creating a line which weaves from side to side, where you can hopefully see competitive play within games and whether one side or the other is winning most of the points. In addition, following the line from side to side echoes the often gently-mocked movement of a person's head when they are watching a tennis match too.
And that's it from me for this week! I'll leave you with a quick reminder of this month's challenge which features IndyCar. The data can be found here. I'm definitely going to take the opportunity to learn something about a sport that I don't know well, and hopefully create something fun & interesting at the same time. I look forward to seeing what you all come up with!
Mo & the rest of the #SportsVizSunday team