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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Gotta rank’em all:
What is the best Pokémon?

Pokémon Go has made the whole world gone wild on the hunt for those cute little creatures. After catching hundreds of Weedles, Rattatas and Pidgeys, I got a bit tired and thought it is time to do some Pokémon science.

Naturally, the whole Pokémon hype has already led to several interesting analyses with available data mainly from the awesome PokeAPI. For instance, this blog post about a cluster analysis of the original 151 Pokémon or this extended analysis of all 721 available Pokémon.

Since clustering is boring, I will do something more exciting and try to rank Pokemon according to their strength with a little bit of help from my own research.

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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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New Domain, New Design, New Direction

If you are regularly reading this blog, you might have noticed a few things. First, I migrated the blog from blogger to my own domain and I am now using wordpress. I also changed the design a bit (might still be changed a bit in the future).

Again if you regularly read this blog you might have noticed that my plots are all rather inconsistent when it comes to formatting. I am working on a generic style I want to use from now on, which is hopefully a bit distinctive for my blog. I also would like to add more tutorial-like entries, such that you can redo the analysis yourself. I will also start uploading my code to github. This should be seen as a try to become a little more “professional”, yet still be easy to digest.

The next (mildly) exciting posts are currently being written and I hope you stay tuned and keep reading my blog.

Analyzing National Geographic Covers

Purely by chance and random surfing, I ended up here, staring at old covers from National Geographic. I was wondering if it is possible to analyze the evolution of the covers in some mildly scientific way. If you accept the fact that pictures are nothing else than three dimensional matrices , you can do quite a lot of things with them.
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