The csgo package provides data parsing, analytics and (soon) visualization capabilities for Counter-Strike: Global Offensive (CSGO) data. In this repository, you will find the source code, issue tracker and other useful information pertaining to the csgo package. Please join our Discord for discussion around the library, along with other resources for esports analytics.
csgo requires Python >= 3.7 and Golang >= 1.16. Python acts as a wrapper for the Go code which parses demofiles.
To install csgo, clone the repository by running git clone https://github.com/pnxenopoulos/csgo. Then, change directories to the newly cloned repository, and install the library by running python setup.py install. For more help, you can visit the installation channel in our Discord.
Using the csgo package is straightforward. Just choose a demofile and have output in a JSON or Pandas DataFrame in a few seconds. Use the example below to get started.
from csgo.parser import DemoParser
# Set parse_rate to a power of 2 between 2^0 and 2^7. It indicates the spacing between parsed ticks. Larger numbers result in fewer frames recorded. 128 indicates a frame per second on professional game demos.
demo_parser = DemoParser(demofile="og-vs-natus-vincere-m1-dust2.dem", demo_id="og-vs-natus-vincere", parse_rate=128)
# Parse the demofile, output results to dictionary with df name as key
data = demo_parser.parse()
# The following keys exist
data["MatchId"]
data["ClientName"]
data["MapName"]
data["TickRate"]
data["PlaybackTicks"]
data["GameRounds"]
# You can also parse the data into dataframes using
data_df = demo_parser.parse(return_type="df")
# You can also access the data in the file demoId_mapName.json, which is written in your working directoryIf you need help with the parser, join our Discord. Please note that CSGO demos are imperfect. Sometimes there are mistakes. If you come across any, do not hesitate to open an issue, or to post it on our Discord, and we can investigate further.
This project uses GitHub issues to track issues and feature requests. You can see open requests here. If you come across a bug, please open an issue, and also consider bringing it to the community's attention in the Discord. Same goes for feature requests. Please checkout the dev branch to work on the library.
Take a look at the following Jupyter notebooks provided in our examples/ directory. These will help you get started parsing and analyzing CSGO data.
You can also look at the following papers which make use of the parser. If using the parser in research, please cite Valuing Actions in Counter-Strike: Global Offensive (first paper). If you have a paper that uses the parser, please let us know in Discord so we can add it!
Xenopoulos, Peter, et al. "Valuing Actions in Counter-Strike: Global Offensive." 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.
Xenopoulos, Peter, et al. "ggViz: Accelerating Large-Scale Esports Game Analysis."
This repository contains code for CSGO analysis. It is structured as follows:
.
├── csgo
│ ├── analytics # Code for CSGO analysis
│ ├── data
│ │ ├── map # Map images
│ │ └── nav # Map navigation files
│ ├── parser # Code for CSGO demo parser
│ └── visualization # Code for CSGO visualization
├── doc # Contains documentation, such as data dictionaries, etc.
├── examples # Contains Jupyter Notebooks showing example code
└── tests # Contains tests for the csgo package
This project is made possible by the amazing work done in the demoinfocs-golang and gonav packages. To fix errors brought about in the gonav package from Go 1.14, we provide an updated version in the gonavparse.
Special thanks to arjun-22 for his work on the stats module and expanding test coverage.