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What data is available?

The Competitive CS:GO Gameplay Data Set contains Competitive Counter-Strike: Global Offensive (CS:GO) match data from matchmaking, FACEIT, and other third-parties. Contains full player telemetry and timestamped game events.

How can I get started?

With the tutorial you will learn how to do some real CS:GO data science with data from the public data set. Once you complete the tutorial, you may use the same repository to bootstrap you own project!

Why are you doing this?

We want to see the amazing stuff you'll do with it! Working with AI researchers using CS:GO to solve really hard problems inspired us to contribute back and unlock this potential for anyone.

We hope this work can fuel everything from one's first data science experience, to their next hackathon, school project—all the way to groundbreaking research.

Who can use the data?

Anyone with a use case that adheres to the license.

The full license is presented and agreed to when you subscribe to the data set, but it's basically the CC BY-NC-SA 4.0 license, which means you may not use the data for commercial purposes, you must attribute, and you must use the same license for any derived work.

Does it cost money?

The data set is released for free, however, the process of exporting the data will incur standard AWS fees for using the cloud provider's storage and bandwidth.

See the Cost FAQ for details.

Why do I need to subscribe?

The AWS Data Exchange, which is the cloud platform hosting the data set, requires users to subscribe for access to hosted data products. On subscribing, you must agree to our version of the CC BY-NC-SA 4.0 license. The subscription to data is always free.

Are you selling my data?

Absolutely not! The data set is free and for the community. We hope the data will be used for cool stuff, and for future improvements to Coach!

Does this mean everyone will know how n00b I am?

Nope! Not from here at least. All player data is thoroughly anonymized: it is impossible to determine a player's identity, virtual or otherwise.

Can I opt-out?

Currently we do not provide a way for users of to opt-out of inclusion in the data set. You may, however, ask for your account to be deleted, which will stop inclusion of future matches.

Please note that even though your match data is in the data set, it is not traceable back to your player identity.

I have more questions?

That wasn't a question. But it's ok, you can reach out in the Dojo channel on Discord. We want to help you and get your feedback!

Technical FAQ

What are the developer tools?

Are the developer tools stable?

Mostly. We don't anticipate majors changes before releasing version 1 of our libraries. During the initial period following the announcement, we will be working with early adopters to squash bugs and smooth out the APIs. Please report any issues on GitHub.

Libraries follow the principals of semver. Beta releases may see breaking API changes before their final release, while release candidates will avoid this unless absolutely necessary.

Python projects use the PEP 440 standard version format of 1.2.3 for stable releases, 1.2.3b0 for beta releases, and 1.2.3rc0 for release candidates. This is a small cosmetic difference, but otherwise, the semantic meaning will follow semver.

What do I need to use the developer tools?

The minimum tutorial requirements are Python and Poetry.

Can I use any version of Python?

No. For the best experience, we recommend using the latest version of Python 3.9

To install an isolated Python version, we recommend pyenv for Linux and Mac, or pyenv-win for Windows.

Version 3.10 may work, but many libraries are still lagging behind on full support, which means installing dependencies may take longer, require additional build tools, or fail. Additionally, Python 3.10 is not yet supported by all cloud providers, including AWS Lambda. When 3.10 has wider adoption, we will officially support it, but for now we only test on 3.9.

Why do I need to use Poetry to install dependencies?

Poetry guarantees each project runs in an isolated environment with pinned dependencies. This is the only way to ensure reproducible tests and builds.

You can try to use an alternative Python package manager, but we cannot offer support.

Can I use my favorite editor or IDE?

Yes. If your editor or IDE integrates with Python, ensure you refer to its documentation for using it with Python virtual environments.

Can I contribute?

Yes! Please use Discord or GitHub issues. We will consider additional forums like GitHub Discussions as the community grows.

To contribute code or documentation, please open a corresponding issue first before opening a pull request. When contributing obvious fixes, you may open a pull request directly.

Can I use $favorite_language?

Yes! The current provided tooling is in Python, but all of the underlying data is stored as Apache Parquet with additional metadata in JSON.

Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Parquet is available in multiple languages including Java, C++, Python, etc...

If you want to use another language, we still recommend stating with the tutorial. This is the easiest way to download the data and create new tomes, which are just more Parquet and JSON files. Tomes are economical views of the data tailored for specific analyses.

Cost FAQ

How much will AWS charge me?

FPS Critic Inc., owner of, is not liable for any AWS costs you incur.

Step 7 of the tutorial can help you estimate your costs, We provide those and the estimates below for convenience, but we do not guarantee their accuracy or applicability to your AWS account.

TL;DR: It costs about $1.50 to download a day of data, or $4-5 per 1,000 matches. Downloading tomes is much less expensive. Tomes contain slices of features across many matches.

How should I optimize my costs?

The data set is structured to help you control and optimize your costs. First, it is split into daily revisions which may be downloaded individually. Second, we maintain a companion tome data set which is very economical.

Consider these steps:

  1. AWS has a free tier which may reduce or the eliminate the cost to get started.
  2. If you can use available tomes for your project, don't bother downloading individual match data. Most tomes cost pennies to download and contain data for across many matches.
  3. If the available tomes are not sufficient for your project, estimate how much data you actually need by starting with one day. Remember that one revision is one day worth of data.
  4. Be careful when downloading a large number of revisions at once. You might go over the free tier part-way though the export process and have difficulty stopping the jobs. We recommend you limit each export batch to one month intervals.
  5. If you share a tome generator with us, we may be able to make the tome for you and share it back to the data set. This would prevent you from needing to download the entire csds data set which is about 2TB.

Where can I get financial support?

  • If you are a student, ask your department or advisor if they can support you.
  • If you are a professional data scientist, ask your organization if they can fund this as a hackathon project or self-guided learning project.
  • Ask AWS for credits. Your company, organization, university, or department may have a relationship with AWS. If not, talk to your AWS account manager and they may help you get some credits.
  • Talk to us. We can't promise financial support, but we can brainstorm a solution together.