Suite of analytical CLI tools meant to help analyse FAF games (currently working only on 1v1 ladder games).
Here are some charts made using fafalytics, Colab and Seaborn
Boxplot of Actions Per Minute (APM) bucketed by the player's FAF rating. This plot analyses about half a million 1v1 ladder game replays.
Lineplot showing the APM over time of Tagada, a top rated player.
Scatter plot showing the coordinates of ~all player commands issued during a game on Open Palms, a popular game map.
Every dot represents a command given by the blue or orange players. The size of the dot is the number of units instructed with this command.
Note: the X-axis is reversed in this chart, but if you ignore that you can see the shape of the map coming through the plot.
Boxplot showing the area of the map covered by commands after 5 minutes of playing, bucketed by player rating. We can see top players cover more of the map with their activity.
$ fafalytics datastore start
$ fafalytics fetch games /tmp/fafalytics
$ fafalytics load /tmp/fafalytics/*.json
$ fafalytics fetch replay-urls /tmp/fafalytics | xargs wget
$ fafalytics extract /tmp/fafalytics/*.fafreplay
$ fafalytics export /tmp/fafalytics/result.parquet curated
This package has a single multi-command executable called fafalytics. It's
meant to help aspiring game analysts follow this process:
overview digraph G { "datastore start" -> "fetch games" "datastore start" -> "fetch replay-urls" "fetch games" -> "load" "fetch replay-urls" -> unpack "fetch replay-urls" -> extract unpack -> extract extract -> export load -> export export -> colab export -> bigquery colab, bigquery [shape=box] log } overview
Each of these is a fafalytics subcommand, other than Colab and
BigQuery which are external tools. Obviously you don't have to use these,
after the export step do whatever you want with the data.
You can learn more about each of these commands using the --help argument, but
basically the logical flow is something like...:
- Start the fafalytics document store (currently Redis)
- Get JSON dumps of Game models models from api.faforever.com
- Load the metadata from the JSON dumps into the datastore
- Get a list of replay-urls and download them somehow (e.g.
| xargs wget) - Optionally unpack the replays Pre-unpacking the replays makes them ~10x faster to read on subsequent reads, which is probably useful if you're hacking on the code
- Extract features from (possibly unpacked) replay files into the datastore
- Export store into CSV/Parquet
- Use the exported data in BigQuery/Pandas/etc
One last command that merits some discussion is fafalytics log. Since this is
a multi-tool sometimes used as a short-lived CLI program and sometimes in a
multiprocessing pipeline-like environment, I built a lightweight logging
mechanism that logs output to the datastore. The log command prints whats in
the datastore log, which can come in handy when running 100 fafalytics
instances in parallel to extract 500K games over an hour or so.
Based in part on work by Askaholic (and with special thanks for answering my questions), norraxx, spooky, and others.