The Shifting Meta

I wanted to practice data analysis in R and decided to take closer look at the last OCTGN data set that was made available in Feb 2015 thanks to db0.

This first preliminary graph that shows a global view of the meta over time, i.e. changes with each new release (from Trace Amount to The Source):

  • Each frame is a snap shot of the meta for a specific RELEASE (indicated in
    the title) of all players on OCTGN.
  • Each column is a single RUNNER.
  • Each row is a single CORP.
  • Each circle represents the specific matchup between the runner and the corp
    of the intersection (e.g. NEH vs. Andromeda).
  • The size of each circle indicates MATCHUP FREQUENCY during a specific release (i.e number of games of a specific matchup divided by total number of games).
  • The color of each circle represents the CORP WIN RATE of that matchup during a specific release, with light grey representing 50% (i.e split), shades of blue > 50%
    (i.e. favorably for corp), and shades of red a corp win rate < 50% (i.e.
    favorably for runner).

Here some general guidelines on how to interpret the graph:

  1. If a lot of circles in a single row are large then the corresponding corp is
  2. If a lot of circles in a single row are blue then the corp is successful.
  3. Correspondingly, large, red circles in a single column indicate a popular and
    succesful runner.
  4. If a lot of circles in all rows and columns are blue then corps dominate
    the meta, if red then runners.

Some stray observations:

  • Newly released IDs are initally popular.
  • Notice how the “Stronger Together” row is mostly red? Always.
  • The meta drastically shifts blue when Honor and Profit is released.
  • After its release, NEH surges while MN and TWIY decline.

The next goals for further analysis are:

  • Getting data for later releases.
  • Implement interactive graphs (e.g. a slider to manually switch between releases, look only at the top 25% of players).

What trends and patterns do you see in the data? What parameters would you like to explore? Please let me know any feedback in the comments!

UPDATE: Here an interactive version of the graph.

UPDATE #2: The interactive version now also contains a table view of the data that can be sorted and searched.


This is incredible! Thanks for your work on this. Moving forward I think using data from would be the way to go, but I’m not sure if their data dumps are as easily useable as db0’s were.

I would say the frames advance to the next far too quickly. I would prefer a solid 10s each frame, maybe 8s


Thanks! I am trying to get in touch with Minh and see what he says. On FB Netrunner Geeks, Ajar told be me that they use a MongoDB database so I think it should be manageable (Ajar posted articles on Netrunner stats on Stimhack some years ago).

Here a slower version of the gif:


Here an interactive version of the graph with a manual slider to select the release.


This is extremely cool! Thanks for taking the time to put this together. As a fellow R user, I’m impressed, especially since I rarely dip into this side of R (namely interactive graphics). Do you mind sharing the code you used to put this together?

I am glad to share the code, you can find it here on GitHub! It is a shiny application, and the tutorial is a good place to start (especially part 1 if you can only spare 45 min).

Note: I just added some more functionality to the application, i.e. a sidebar layout, and tab layout that also includes a table view of the data and the description I originally provided in the post here.

Let me know if you have any questions!

The interactive version now also contains a table view of the data that can be sorted and searched.