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Good afternoon fellow #SportsVizSunday fans and welcome to another roundup of the best sporting visualisations that have been created this week.
The keen eyed and sharp witted amongst you might have realised that this blog is being written on a Monday but I can promise you it will still contact the traditional content that you love!
Kicking off this week, Tedy Iskander has celebrated Gregoria Tunjung's maiden badminton World Tour title. Not only was it her first title, but she beat Carolina Marin (previous world number 1, three time world champion and Olympic gold-medallist) in the semi-final and PV Sindhu (previous world number 2, world champion and two-time Olympic medallist) in the final! That's quite a way to do it. I like the way Tedy has shown the name and nationality, and his layout is clear enough that I can follow it even without the curvy lines!
Over on LinkedIn, Davide Iacobelli showed off his new tracker for the Most Valuable Player (MVP) in the NBA. Showing the pictures of the players is an enjoyable alternative to using their names as labels, especially with the reference point of the current top 3 players on the left-hand side to compare against. I am also a big fan of butterfly charts as they make comparisons quite easy, although it is important to make sure that your axis are aligned so that the scale doesn't change from one side to the other!
Davide's tracker can be found on Tableau Public here:
The 30 Day Chart Challenge is going on throughout April and there have been a number of great sporting visualisations too. The Chart Challenge is a great way to test your skills, experiment with some new tools or visualisation types, and connect with other data people. It's well worth following along and maybe you'll be inspired to take part next year!
First up is a pair of visualisations from Marc Soares. The first is his distributions of round scores at The Masters golf tournament recently. I was surprised that the distribution looked quite similar for each round, although the high and low outliers moved a bit. I liked Marc's decision to reverse the axis so that lower scores, which are better, were at the top instead of the bottom.
Marc's second is a look at how the percentage of fastballs and sliders thrown by baseball pitchers have changed since 2022. It's interesting that although fastballs have continually declined, sliders have only really started increasing since 2014. I don't know enough about baseball to know why this might be (or what the pitchers were throwing instead!) but please do reach out if you know.
Marcel Ferreira chose to use a heatmap design to show which Brazilian Serie A football teams finished the season with positive, negative or neutral goal difference. The simplicity of this design allows the journey of each team to shine and made me want to go and look at who had won the league each year too (more on that in a second).
I said that Marcel's first visualisation left me wanting more and fortunately, Marcel has already done another graph on the same topic for the Chart Challenge. This one shows the range of finish positions for each club in the league over the same time period using a dumbbell graph. Excellent stuff!
Sticking with a football theme, Roppick has visualised the Elo rating of Rennes football club since 1945 (for those new to Elo basically the higher the score, the better the team). They've highlighted a lot of important first matches, when the club won the league or was relegated, and which manager was in charge at which time. There is so much going on here, and I love visualisations like this (as well as the Elo methodology too). I haven't read through their thread explaining more about the club and its results yet but I am looking forward to doing so because the club looks like it has had a fascinating history.
A periodic regular to our pages, Todd Whitehead has used a bar graph to explore the different heights of male basketball players by their position. Todd has really shown how a fairly simple graph type can be utilised to show rich detail by including the medians and labelling some famous outliers. Plus, he has used the orange of a basketball which looks really engaging!
Continuing the basketball theme, Ansgar Wolsing has created a great scatter plot for the Chart Challenge showing which NBA teams have performed better or worse than last season. I like how they've shaded half the graph to help distinguish better from worse, as well as using labels and colours.
There's also a couple of other things that you might find useful this week. Firstly is this NBA dataset from Dom Samangy. Dom has collected all of the player play type data from 2015 and shown a little example of the sort of magic you can do with this dataset!
And lastly, our very own CJ Mayes has been a busy bee once again and created not one but two tutorials!
The first uses Alteryx to create some rather good looking football shot maps, and the second builds upon those techniques to automate the process of creating football match reports! Both are well worth a read if you're interested in shot maps or automating report-building - even if you use different software, there is plenty to inspire you.
And that's everything from us for this week. I hope you all have a good week and keeeeeep vizzzing (especially using the #SportsVizSunday hashtag!)
Mo & the #SportsVizSunday team