Boxcars is a Rocket League replay parser library written in Rust.

• ✔ Safe: Stable Rust with no unsafe
• ✔ Fast: Parse a hundred replays per second per CPU core
• ✔ Fuzzed: Extensively fuzzed against potential malicious input
• ✔ Ergonomic: Serialization support is provided through serde

See where Boxcars in used:

• Inside the rrrocket CLI app to turn Rocket League Replays into JSON
• Compiled to WebAssembly and embedded in a web page
• Underpins the python analyzer of the popular calculated.gg site

Below is an example to output the replay structure to json:

use boxcars::{ParseError, Replay};
use std::error;
use std::fs;
use std::io::{self, Read};

fn parse_rl(data: &[u8]) -> Result<Replay, ParseError> {
    boxcars::ParserBuilder::new(data)
        .must_parse_network_data()
        .parse()
}

fn run(filename: &str) -> Result<(), Box<dyn error::Error>> {
    let filename = "assets/replays/good/rumble.replay";
    let buffer = fs::read(filename)?;
    let replay = parse_rl(&buffer)?;
    serde_json::to_writer(&mut io::stdout(), &replay)?;
    Ok(())
}

The above example will parse both the header and network data of a replay file, and return an error if there is an issue either header or network data. Since the network data will often change with each Rocket League patch, the default behavior is to ignore any errors from the network data and still be able to return header information.

If you're only interested the header (where tidbits like goals and scores are stored) then you can achieve an 1000x speedup by directing boxcars to only parse the header.

• By skipping network data one can parse and aggregate thousands of replays in under a second to provide an immediate response to the user. Then a full parsing of the replay data can provide additional insights when given time.
• By ignoring network data errors, boxcars can still provide details about newly patched replays based on the header.

Boxcars will also check for replay corruption on error, but this can be configured to always check for corruption or never check.

To run the boxcar benchmarks:

cargo bench

# Or if you want to see if compiling for the
# given cpu eeks out tangible improvements:
# RUSTFLAGS="-C target-cpu=native" cargo bench

Since Boxcars allows you to pick and choose what to parse, below is a table with the following options and the estimated elapsed time.

Special thanks needs to be given to everyone in the Rocket League community who figured out the replay format and all its intricacies. Boxcars wouldn't exist if it weren't for them. I heavily leaned on implementations in rattletrap, RocketLeagueReplayParser, and Bakkes' replay parser. One of those should be your go to Rocket League Replay tool, unless you need speed, as those implementations are more mature than boxcars.

• Rattletrap is a binary that ingests rocket league replays and outputs JSON, while boxcars is a lower level parsing library. Boxcars underpins Rrrocket, a cli binary that outputs JSON similar to Rattletrap
• Rattletrap can roundtrip replays (convert them into JSON and then write them out back to a replay losslessly). Boxcars is focussed on parsing replays.
• In part due to allowing roundtrip parsing, rattletrap JSON output is 2x larger than boxcars (rrrocket) even when accounting for output minification.

Below are some differences in the model:

rattletrap:

"properties": {
  "value": {
    "BuildID": {
      "kind": "IntProperty",
      "size": "4",
      "value": {
        "int": 1401925076
      }
    },
  }
}

boxcars:

"properties": {
  "BuildID": 1401925076
}

rattletrap:

"actor_id": {
  "limit": 2047,
  "value": 1
},

boxcars:

"actor_id": 1

rattletrap:

"value": {
  "spawned": {
    "class_name": "TAGame.GameEvent_Soccar_TA",
    "flag": true,
    "initialization": {
      "location": {
        "bias": 2,
        "size": {
          "limit": 21,
          "value": 0
        },
        "x": 0,
        "y": 0,
        "z": 0
      }
    },
    "name": "GRI_TA_1",
    "name_index": 0,
    "object_id": 85,
    "object_name": "Archetypes.GameEvent.GameEvent_Soccar"
  }
}

boxcars:

"actor_id": 1,
"name_id": 1,
"object_id": 85,
"initial_trajectory": {
  "location": {
    "x": 0,
    "y": 0,
    "z": 0
  },
  "rotation": null
}

While rattletrap provides convenience conversions, boxcars omit them in favor of a more raw view of the replay:

• to derive object_name: replay.objects[x.object_id]
• to derive name: replay.names[x.name_id]

Attribute updates:

rattletrap:

{
  "actor_id": {
    "limit": 2047,
    "value": 7
  },
  "value": {
    "updated": [
      {
        "id": {
          "limit": 98,
          "value": 34
        },
        "name": "Engine.PlayerReplicationInfo:PlayerName",
        "value": {
          "string": "Nadir"
        }
      }
    ]
  }
}

boxcars:

{
  "actor_id": 7,
  "stream_id": 34,
  "object_id": 161,
  "attribute": {
    "String": "Nadir"
  }
}

To derive rattletrap's name for the attribute use replay.objects[attribute.object_id]

Boxcars contains a fuzzing suite. If you'd like to run it, first install cargo-fuzz

cargo install cargo-fuzz

There are several scenarios to fuzz (cargo fuzz list), and the best one to run is no-crc-body, due to all aspects of the replay being fuzzed without a crc check:

cargo +nightly fuzz run no-crc-body