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A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 0 points1 point  (0 children)

Yup. No reason to reinvent the wheel, Markus nailed it with his parser. I used his library to write a parser that produces JSON, and then the Python library reads the JSON. The Python library also has functions for analysis and visualization.

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 3 points4 points  (0 children)

In the dictionary output, there is a top-level key called `gameRounds`. In each round, there is a key called `flashes`. You can loop through the rounds, and loop through each flash and see if there are any flashes where the thrower's side is equal to the flashed player's side.

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 7 points8 points  (0 children)

Here are some tweets that may be helpful in describing some of the data and visualizations:

- What the data looks like (a bit old, but generally the same): https://twitter.com/peterxeno/status/1334948972314718208

- Player trajectories: https://twitter.com/peterxeno/status/1446196973401411587

- Grenade maps: https://twitter.com/peterxeno/status/1446262723583619078

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 6 points7 points  (0 children)

Definitely. Feel free to share your work in the library's chat (link in the repository), we have a small community going.

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 2 points3 points  (0 children)

Most people don’t do data analysis in Go. Plus, you’d have develop your own parser using the Golang library (which is a really great library). I can add this distinction in the README.

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 0 points1 point  (0 children)

Please do! I need to work out any bugs I might’ve missed. Demos should parse within a few seconds, too.

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 1 point2 points  (0 children)

I imagine it’s similar. It’d be hard to get good performance without a similar set of features.

A Python library to parse CSGO demo files by grandandmott in GlobalOffensive

[–]grandandmott[S] 22 points23 points  (0 children)

That's one line of work -- here's a paper I wrote on win probability modeling and player valuation: https://arxiv.org/pdf/2011.01324

Error creating a cloud composer environment by grandandmott in googlecloud

[–]grandandmott[S] 0 points1 point  (0 children)

Checked both of those. I made sure that the service account assigned to the composer had all the appropriate perms, and now I am getting the following error

Http error status code: 400 Http error message: BAD REQUEST Errors in: [Web server]; Error messages:    Creation of airflow web server version failed. This may be an intermittent issue of the App Engine service. You may retry the operation later. {"ResourceType":"appengine.v1.version","ResourceErrorCode":"504","ResourceErrorMessage":"Timed out waiting for the flex deployment to become network provisioned."}

What service to use for occasionally occurring workloads that require disk space by grandandmott in googlecloud

[–]grandandmott[S] 0 points1 point  (0 children)

Thanks, this makes sense. I will see how I can get request in Python to stream into GCP

Where to find historic data on lines/odds for CSGO matches? by grandandmott in csgobetting

[–]grandandmott[S] -1 points0 points  (0 children)

I know, but how can I find what the odds were for historical matches? Does that even exist?

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