Approved by celticpride
Traditionally, usage data has been analysed in month-by-month slices, as and when new statistics are published. This is great for looking at short term changes, for example the impact of a particular centralising Pokemon being banned from a tier. What about longer term trends? You need a visual tool. I have written v2 of the tool I wrote a year ago. It can be found at the URL below:
http://pokestat.org.uk
I haven't played properly since DPP, so I'm very curious to see what people who have more context of the various metagames in the modern game can come up with using this tool! Please use and abuse it and post your findings back in this thread! If you want some ideas, some of my exploration is in the spoiler section below:
Again, I'm not familiar with Gen 6, so please go ahead explore those ideas and your own. Let us know what you find out! I wish I could post this in a more general thread, as it covers more than just Gen 6 OU, but this seems the most appropriate place that I have permission to post in.
Happy stat hunting!
(Remember - correlation doesn't imply causation, but speculation is always fun!)
Traditionally, usage data has been analysed in month-by-month slices, as and when new statistics are published. This is great for looking at short term changes, for example the impact of a particular centralising Pokemon being banned from a tier. What about longer term trends? You need a visual tool. I have written v2 of the tool I wrote a year ago. It can be found at the URL below:
http://pokestat.org.uk
I haven't played properly since DPP, so I'm very curious to see what people who have more context of the various metagames in the modern game can come up with using this tool! Please use and abuse it and post your findings back in this thread! If you want some ideas, some of my exploration is in the spoiler section below:
Take the most used Pokemon last month, Landorus-Therian. I noticed how its usage spiked a lot in April - why? Perhaps one of it's major counters has been removed - so I stuck Ferrothorn on the graph and noted how it's usage has dropped this month, but again, why? To a regular, the answer is obvious, Volcanion. It's the secondary effect that I find most interesting, whereby the introduction of Volcanion indirectly caused Landorus usage to spike!
Consider the question: Did the introduction of the primal forms of Groudon and Kyogre have an impact on the Ubers metagame? Select Gen 6 Ubers and plot Groudon, Kyogre, Groudon-Primal and Kyogre-Primal on the graph. When the Gen 3 remakes were released, the graphs remained pretty much continuous, as if everyone pretty much just replaced the originals with the Megas. So perhaps there was little impact, or perhaps you can throw a few more Pokemon onto the graph and prove me wrong?

Consider the question: Did the introduction of the primal forms of Groudon and Kyogre have an impact on the Ubers metagame? Select Gen 6 Ubers and plot Groudon, Kyogre, Groudon-Primal and Kyogre-Primal on the graph. When the Gen 3 remakes were released, the graphs remained pretty much continuous, as if everyone pretty much just replaced the originals with the Megas. So perhaps there was little impact, or perhaps you can throw a few more Pokemon onto the graph and prove me wrong?

Again, I'm not familiar with Gen 6, so please go ahead explore those ideas and your own. Let us know what you find out! I wish I could post this in a more general thread, as it covers more than just Gen 6 OU, but this seems the most appropriate place that I have permission to post in.
Happy stat hunting!
(Remember - correlation doesn't imply causation, but speculation is always fun!)
The new version addresses a number of problems, and pieces of feedback:
- I now cover the full set of generations and tiers covered in the usage stats
- No data gaps (the previous version interpolated as it was missing data from certain months)
- New published stats will be live on this site within an hour of being published
- More extensible (the backend is storing both "real", "raw" stats and also stats cut at particular ladder rankings, which could be exposed relatively easily)
- Github repository
- The backend is a rails server:
- I am familiar with the technology so it was easy for me to put this together in a couple of days
- The data model and use case suit the server / SQL backend very well (I spend most of my work time dealing with timeseries data kept in SQL databases, which sped things up)
- cron configured to run a loader (which runs in the rails environment, again making things easier) once every 30 minutes, and only loads newly published data
- The app is hosted on Digital Ocean, their pre-canned rails with postgres means I can go from a development environment to a hosted service in about an hour
- The frontend uses ChartJS to plot, as well as some simple jQuery and bootstrap