Football analyst network vis: New and improved! feedly

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Football analyst network vis: New and improved!
// Wallpapering Fog

My original visualisation of the football analysis community on Twitter generated a fair bit of interest, so it’s time for a new and improved version.

We had one recurring question the first time around: “Where am I on the graph?”. It badly needed a search function and thanks to @balbezit, it now has that and whole lot more. Twitter really is a fabulous learning tool! I’d tweeted an early static image of the updated network and got this reply…

balbezit.png

I didn’t know how to do that, or even that you could. But I do now.

I’ve also extended the starting group of analysts to the 110 accounts on this list. If you’re followed by at least two of those accounts, then you should appear in the network graph somewhere. Following loads of them doesn’t count – they have to find you interesting too!

Here’s step one. You get a bigger node if more people in this community are following you. It’s not sized by follower count in general, otherwise Barack Obama and Cristiano Ronaldo would have the biggest circles, by miles. More closely linked people will cluster closer together.

Mono%2B-%2Bsmaller.png

And next (again via a tip from @balbezit), we can shade the communities in different colours. This is an automatic algorithm in Gephi and I think it’s quite effectively separated everybody by their interests.

Broadly, we get…

Red: Core of (largely) Premier League statistical analysts
Light blue: Professional analysis, including OptaPro and Prozone
Green: Journalism
Yellow: The transatlantic connection
Dark blue: Wider interests, including marketing and data vis (I’m in this one)
Purple: FiveThirtyEight

Multicoloured%2B-%2Bsmaller.png

For the interactive version, go here.

It’s a big web page. You will have to give it a while to load.
If you use Internet Explorer then you deserve the issue that it will look blurry, rather than crisp and easy to read. Download a proper browser.

Try clicking nodes and zooming and panning around the view!

Help! I can’t find my own account!

You need to use the search box on the left. Search for your display name, not your Twitter username (i.e. I’d search for “Neil Charles”, not “@data_monkey”)

The chart will filter to show your personal following network, but your own node still might not be obvious, if it’s small. Near the top of the bar that’s popped up on the right hand side of the page, hover over your name. Voila! There you are.

Thanks go to…

@balbezit for the tips, Gephi for a brilliant bit of visualisation software and to Scott Hale for his fantastic Sigma.js exporter that was used to build the interactive vis.

—-

Shared via my feedly reader

Sent from my iPad

Football analyst network vis: New and improved! feedly

—-
Football analyst network vis: New and improved!
// Wallpapering Fog

My original visualisation of the football analysis community on Twitter generated a fair bit of interest, so it’s time for a new and improved version.

We had one recurring question the first time around: “Where am I on the graph?”. It badly needed a search function and thanks to @balbezit, it now has that and whole lot more. Twitter really is a fabulous learning tool! I’d tweeted an early static image of the updated network and got this reply…

balbezit.png

I didn’t know how to do that, or even that you could. But I do now.

I’ve also extended the starting group of analysts to the 110 accounts on this list. If you’re followed by at least two of those accounts, then you should appear in the network graph somewhere. Following loads of them doesn’t count – they have to find you interesting too!

Here’s step one. You get a bigger node if more people in this community are following you. It’s not sized by follower count in general, otherwise Barack Obama and Cristiano Ronaldo would have the biggest circles, by miles. More closely linked people will cluster closer together.

Mono%2B-%2Bsmaller.png

And next (again via a tip from @balbezit), we can shade the communities in different colours. This is an automatic algorithm in Gephi and I think it’s quite effectively separated everybody by their interests.

Broadly, we get…

Red: Core of (largely) Premier League statistical analysts
Light blue: Professional analysis, including OptaPro and Prozone
Green: Journalism
Yellow: The transatlantic connection
Dark blue: Wider interests, including marketing and data vis (I’m in this one)
Purple: FiveThirtyEight

Multicoloured%2B-%2Bsmaller.png

For the interactive version, go here.

It’s a big web page. You will have to give it a while to load.
If you use Internet Explorer then you deserve the issue that it will look blurry, rather than crisp and easy to read. Download a proper browser.

Try clicking nodes and zooming and panning around the view!

Help! I can’t find my own account!

You need to use the search box on the left. Search for your display name, not your Twitter username (i.e. I’d search for “Neil Charles”, not “@data_monkey”)

The chart will filter to show your personal following network, but your own node still might not be obvious, if it’s small. Near the top of the bar that’s popped up on the right hand side of the page, hover over your name. Voila! There you are.

Thanks go to…

@balbezit for the tips, Gephi for a brilliant bit of visualisation software and to Scott Hale for his fantastic Sigma.js exporter that was used to build the interactive vis.

—-

Shared via my feedly reader

Sent from my iPad

The usual “lull” time

Like many others I am experiencing a lull in postings on this site. Not so much because I am no longer active in data visualization nor because I have lost interest in soccer recently. I am just spending more time on learning more about data visualization through #IVMOOC 2015, getting more familiar with Tableau (Public as well as at work) and still trying to keep up with all interesting thing happening in data vis world. And I am still trying to post to two other blogs:

  • on drawingthedata.weise.ca I post data vis related items I find somewhere, and then organize them in a way that is useful for me. Although not a site I tend to hide from the public (it may be useful for someone else, who knows) but it is focussed on meeting my personal needs through projects, tool collections, etc.
  • notime2read.com is a site I set up to display data visualizations I have created, ideally with some context on how I got to the end result, showing steps in between, design thoughts, etc.

Cheers,

RJ

Football / soccer pitches shading

I have always been interested in how field keepers make the bands if different colours of green on football (soccer) pitches. I heard and read it has to do with the direction of mowing rolling, and as a result how the grass leaves reflect the sunlight.

This was really noticable in the game I watched a few weeks ago as the shading changed 90 degrees as a result of changing camera direction and sun / shade areas.