This is insanely cool! Very impressed you managed to implement a full parser in Rust. I implemented a basic one in Rust a while back: https://github.com/Zephyr

This is insanely cool! Very impressed you managed to implement a full parser in Rust. I implemented a basic one in Rust a while back: https://github.com/ZephyrBlu/rust-parser https://github.com/ZephyrBlu/rust-parser And a full one in Python with a few bells and whistles ages ago: https://github.com/ZephyrBlu/zephyrus-sc2-parser https://github.com/ZephyrBlu/zephyrus-sc2-parser Don't maintain either of them though :(, and the Rust one is super rough. SC2 is a very interesting area for data analysis, but at the same time I found it very challenging. There is so much nuance and inconsistency across games it can be really hard to do accurately do things like categorize builds or measure build timings. The area I ended up focusing on was builds, and I feel like I did some interesting stuff there: https://sc2.gg/reports/top-openings-2022/ https://sc2.gg/reports/top-openings-2022/. I found personal statistics less interesting than aggregate statistics. Even pro games are very volatile, ladder games even more so. Extremely hard to get reliable signal out of them if you're trying to track things across games. Even simple things like Collection Rate are poor indicators without significant categorization work (Matchup, build, opponent build, etc).