Question 1

How do I parse a Rocket League replay file with Boxcars?

Accepted Answer

Use the ParserBuilder to configure parsing levels, then call parse() on the replay data. The quick start example shows how to parse both header and network data and serialize to JSON with serde.

Question 2

Boxcars vs rattletrap: which should I use?

Accepted Answer

Choose Boxcars for high-performance parsing and raw data access in Rust; opt for rattletrap if you need roundtrip functionality or a more abstracted JSON output. Boxcars is faster and produces smaller JSON, but requires manual derivation for some properties.

Question 3

Can Boxcars handle corrupted replay files?

Accepted Answer

Yes, it includes configurable corruption checks and is extensively fuzzed to handle malformed input. You can set it to always, never, or on-error check for corruption, ensuring robustness in production.

Question 4

How to extract player stats from replays using Boxcars?

Accepted Answer

Parse the replay to access network data, then iterate through actors and attributes to find player-related information like scores and goals. Since it provides raw data, you'll need to map IDs to names using the replay object and name tables.

Question 5

Is Boxcars compatible with Python?

Accepted Answer

While Boxcars is a Rust library, it underpins the Python analyzer on calculated.gg via bindings. For direct use, you'd need to integrate it through WebAssembly or custom FFI, which adds setup complexity.

Question 6

What's the performance for parsing thousands of replays?

Accepted Answer

With header-only parsing, Boxcars can process thousands of replays in under a second, as shown in benchmarks. For full parsing, throughput is around 300 MiB/s, making it suitable for bulk data analysis.

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