Angelique Kerber, No. 1 in women’s tennis…since weeks!

This Monday September 12th will be a historic day for German female tennis. Angelique Kerber will be the first German player since Steffi Graf in 1996 who is ranked number one in the WTA ranking.

Winning the Australian Open in the beginning of this year, reaching the final of Wimbledon and then winning the US Open,  one could definitely say that she finally deserves it.  I would even go a step further and say it is overdue for a few weeks! To “prove” this claim, I grabbed all WTA matches since 1968 (yeah, I know Angelique wasn’t even alive then) until 29 August 2016 from here and here and built my own women’s tennis ranking with the power of Google’s PageRank.

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The Lord of the Rings: The Three Networks

This post is inspired by the star wars social network.

I created the interaction networks of Lord of the Rings characters for all three movies based on the scripts I found online.  The networks capture the story line of all movies surprisingly well and might be a nice gimmick for all Lord of the Rings enthusiasts. Code and network files can be found on github.

I also created interactive versions of the networks where you can drag, click and hover and generally play around a bit. The links to the those versions are below the respective plots.

For those who are interested in technical details of the data extraction and analysis, head down to the Making of section. But let’s start with some visualizations of the networks for the three movies.

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Six Degrees of Zlatan Ibrahimovic

Zlatan Ibrahimovic, Ibracadabra, the self proclaimed god. Or simply Zlatan.  An incredible football player, well known for his shenanigans outside of the football ground. The Internet has created quite a cult around Zlatan and I thought to contribute by conducting the “Six degrees of Zlatan” experiment.
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Soccer Analytics Part 2: Why Al Kuwait SC was (probably not) the Best Team in 2013

In the first part of my Soccer Analytics series, I talked about general statistics of my dataset of the season 2013. As a reminder, i scraped all results from 177 domestic leagues and intra continental cups world wide. This entry will deal with the question “Who was the best team in 2013?” according to network analysis and why the result is (most likely) wrong.

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Not Interesting Networks: The Human Body

“Networks are everywhere” is THE catch phrase of social network analysis. As I said in a previous post, if you are a network scientist everything looks like a network to you.  That’s why I decided to start a series about “everywhere” networks. But not just any. No! In particular those that are just not interesting at all. Although networks might be ubiquitous, that does not always make them worthwhile studying.

With this special kind of networks, I will just do some fancy visualizations and maybe some boring statistical analysis. To emphasize the non-scientific relevance, the visualizations and statistics will be presented with the dearly beloved comic sans.


Body Part Relationships Network

I got the data from here. The description of the data is as follows:

“[…] contributors classified if certain body parts were part of other parts. Questions were phrased like so: “[Part 1] is a part of [part 2],” or, by way of example, “Nose is a part of spine” or “Ear is a part of head.”

Well if that’s not exciting…

Here is a visualization of the network

A cluster analysis revealed, to which class of body parts (upper in light blue, lower in light red) certain body parts belong. The algorithm was not so sure about the belly though.

The following table shows the most important body parts according to some centrality measures and what these measures could stand for.

So the head is the most important body part and the face is full of other things. Good to know.

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