sprocket-rl-parser
An open-source project for decompiling and analyzing Rocket League replays. It exposes a Python API that returns protobuf metadata, JSON output, and a pandas DataFrame for frame-by-frame analysis.
Install
pip install sprocket-rl-parser
Building from source
Source builds require Rust, Python >=3.8, and protoc 29.5. Install the
protoc 29.5 release
for your platform and put its bin directory on PATH, or set PROTOC_PATH
to the compiler executable:
export PROTOC_PATH=/path/to/protoc-29.5/bin/protoc
python -m pip install .
The build regenerates carball/generated from api/. Protoc 29.5 generates
Python code requiring protobuf>=5.29.5,<6.0.0, the runtime range declared by
this project. Other compiler versions are rejected before generation so a
newer system compiler cannot silently produce an incompatible wheel.
Installing a prebuilt wheel does not require protoc.
Quickstart (Python)
import carball
analysis_manager = carball.analyze_replay_file("path/to/replay.replay")
# Protobuf game metadata + stats
proto_game = analysis_manager.get_protobuf_data()
# JSON-friendly dict
json_game = analysis_manager.get_json_data()
# Frame-by-frame data
data_frame = analysis_manager.get_data_frame()
If you only need replay metadata and want to avoid parsing network frames entirely:
summary_manager = carball.summarize_replay_file("path/to/replay.replay")
# Protobuf metadata only
proto_game = summary_manager.get_protobuf_data()
# JSON-friendly dict without frame data
json_game = summary_manager.get_json_data()
CLI
The package installs a carball command for one-off parsing.
carball --input path/to/replay.replay --json output.json --proto output.proto --gzip frames.gzip
Output formats
Protobuf
analysis_manager.get_protobuf_data() returns a game_pb2.Game object. This is the authoritative metadata and stats
output (players, teams, events, goals, etc.). The schema lives under api/ in this repo.
JSON
analysis_manager.get_json_data() returns a JSON-compatible dict matching the protobuf schema. Use
analysis_manager.write_json_out_to_file() to persist the output.
DataFrame (frame-by-frame)
analysis_manager.get_data_frame() returns a pandas DataFrame with a MultiIndex column structure:
- The index is the sequential frame number.
- Columns are tuples like
(object, field)whereobjectisgame,ball, or a player name.
Example: pull ball positions and a single player's positions.
ball = data_frame["ball"][["pos_x", "pos_y", "pos_z"]]
player = data_frame["SomePlayerName"][["pos_x", "pos_y", "pos_z"]]
You can also persist the DataFrame with gzip and read it back:
import gzip
from carball.analysis.utils.pandas_manager import PandasManager
with gzip.open("frames.gzip", "wb") as f:
analysis_manager.write_pandas_out_to_file(f)
with gzip.open("frames.gzip", "rb") as f:
df = PandasManager.read_numpy_from_memory(f)
Advanced options
analyze_replay_file supports additional flags:
analysis_per_goal=Trueto analyze each goal segment independently.calculate_intensive_events=Truefor extra stats that are more expensive to compute.clean=Falseto skip data cleanup if you want rawer frame data.
analysis_manager = carball.analyze_replay_file(
"path/to/replay.replay",
analysis_per_goal=False,
calculate_intensive_events=True,
clean=True,
)
Troubleshooting
- If you see missing or invalid data, try
calculate_intensive_events=Falseandclean=Truefirst. - For reproducible analysis, make sure you are parsing the raw
.replayfile and not a previously decompiled JSON. - If a replay no longer has parsable network frames after a game update, use
summarize_replay_file()ordecompile_replay_header_only()for header-only parsing.
Metadata
Release files for sprocket-rl-parser 1.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sprocket_rl_parser-1.4.0.tar.gz | 16.5 MB | Details |
Built distributions (wheels)
Total release size: 31.3 MB
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