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moseswootten

Weekly Round-Up 10.09.2022

Happy Sunday #SportsVizSunday'ers.


I was one of the 80,000 people who went to watch the England Lionesses play the all conquering USA Women's team in a game of football on Friday night. Although it was technically a friendly, it was billed as the European Champions (England) vs the World Champions (USA) and it lived up to the hype in my opinion. Excellent quality football, a great atmosphere and vocal crowd, and a win for the Lionesses! Good preparation for the World Cup next year.


I also found myself watching some college football (American football this time) on the television yesterday, as the Texas Longhorns took on the Oklahoma Sooners. College football is not something I follow, but fortunately Samuel Epley has come to my rescue!


This is part of his college football visualisation, which has a gorgeous design, and shows the context of the game I watched yesterday. I didn't realise it quite so momentous, both in score and also in snapping the existing win streak.


I'm always interested in the physical attributes of athletes, and Nicolas Mieszaly has looked at the height and weight of the top 100 tennis players since 1973. I like how Nicolas has chosen to display this, using colours, size and lines to differentiate between the world number 1, top 5 and top 100 players.


It's very interesting to see how the top 5 in particular have gone from generally being smaller and lighter than the average top 100 player, to being taller and heavier. I wonder if this stacks up with what tennis fans have seen too.


One of the best way to practice design skills is to have a go at replicating something that you admire, and Daniel Musil has used Tableau to recreate this visualisation from Son of a Corner while adding some interactive functionality.


I think this is a really smart design and the options to switch been expected & actual goals for/against, and whether to just show the difference or actual values, mean that there is lots to see.


Next up is the first of two visualisations from Bo McCready. Following the Seattle Mariner's first appearance in the baseball postseason for 21 years, Bo has created a clever little visualisation showing the playoff streaks and droughts in the MLB. I really like the simplicity of creating lines with dots and it makes it easy to see the history of each team. I hadn't realised it was quite so hard to put together a long streak of playoff appearances.


Bo's second visualisation highlights how Erling Haaland is changing the face of football! He's scored an incredible number of goals in his first season already, and Bo shows just how fast he has started when compared to other top Premier League goalscoring seasons. Haaland's rate has dropped off a bit - he's now on 15 goals from 10 appearances, level with Luis Suarez 13/14 season - and it will be interesting to see whether teams start to work out how to defend against him.


This next visualisation by Bruno Mioto is a good angle on how quickly quarterbacks in the NFL take to throw the ball. My understanding is that quicker is generally better although it is dependent on how the team plays. Lamar Jackson holds on to the ball for longer than most but he also poses a significant threat as a runner with the ball. Tom Brady on the other hand, uses his decades of experience to immediately read the field and get the ball out faster than anyone else.


Kevin Flerlage has sad news for Cincinnati Reds fans, as their team has now lost 100 MLB games. We've featured this visualisation before to answer the question of whether this team is the worst team in baseball history, and I felt it was worth showing again now that they have passed that unenviable milestone.


I'm going to finish with two things from inside #SportsVizSunday towers. The first is CJ's tutorial on how to add another metric to your pizza/coxcomb chart and sort them appropriately. These charts have absolutely taken off in player analytics and you can learn how to make them too with CJ's tutorials!


Last but very much not least, Simon has gathered an excellent rowing dataset for our new challenge. There's lots in there so check it out here (although it might not be so useful for making a pizza chart).



Cheers all


Mo & the SVS team


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