My parser for Rocket League replays.

Rocket League is a competitive video game based on soccer, except each player controls a rocket-powered car.

Usage

Clone the repository, download all dependencies (boxcars, pandas), and call main.py [PATH], specifying the path to the .replay file that you wish to parse.

By default, the program will NOT save the data anywhere, so you will have to tweak the main.py file to do anything with the data. Currently, it simply prints out a condensed version (every 10 frames) of the pandas.DataFrame for the general game.

Background Information

Each match in Rocket League can be saved as a replay to be viewed later. Each replay is characterised by the .replay file extension and contains frame-by-frame data for every actor (an arbitrary name for game objects, such as the cars, the ball, and even game metadata). Of course, different types of actors will have different data associated with them, and this project aims to retrieve and restructure said data for easy use (whether for exploration or analysis).

Please note that this project started out as a fork of the parser for carball, but I quickly found that there was a lot of dead code and an awkward implementation for handling the many different actors (ie. not easily extensible). Therefore, I decided to rewrite it from scratch, which served as great Python practice; and, in the end, my parser was fully compatible with carball, while being slightly faster (at worst, equally performant). However, the carball project was shutdown before I was confident enough to suggest such a massive change...

Project Breakdown

To begin with, the binary .replay file is parsed into JSON via boxcars, which was originally used by the carball project (see above). The initial JSON output is a deeply nested dictionary of actors and their data, which we wish to restructure in a logical manner, described later on.

Then, the initial Replay object is constructed with some easily accessed metadata such as the owner's name, the date, total goals, etc. These are stored under one of the top-level dictionary keys, all of which can be printed with boxcars_replay.keys().

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The subsequent parsing is dependent on config.yml, which primarily specifies the actor types as full class names and whether they should be parsed (otherwise ignored).

About actor class names:

They are found by examining the values under the objects top-level key of the replay.

They are specified in the configuration file to allow for easy access and modification, in case they change or an additional actor needs to be added.

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The ActorParser class is then responsible for classifying actors by type, retrieving data for each actor and ensuring that all actors remain consistent with their objects. The latter is a crucial detail, since actors may be deleted and replaced throughout the replay (eg. the ball is deleted and replaced on every goal/kickoff).

About actor parsing:

For each frame of the replay, I categorise actors by their archetype, which can be thought of as a superclass type for the actor types in config.yml (specifically, I use my custom short types, such as 'Car' and 'Ball', which are also reflected in config.yml).

Then, for each category, I create an array of dictionaries that store the data associated with a given actor object; demonstrated:

{...,
 251:
	{ # Archetypes:
	 'Car':
	   [ # Data per car actor:
		 {'actor_id': 10, 'throttle': 255, ...},
		 {'actor_id': 11, 'throttle': 0,   ...},
		 ...],
	 'Player':
	   [ # Data per player actor:
		 {'actor_id': 0, 'ping': 12, 'name': "Divvy C.",   ...},
		 {'actor_id': 1, 'ping': 19, 'name': "Ballchaser", ...}
		 ...],
	 ...},
 ...}

The actor dictionaries are retrieved by archetype-specific parsers (replay_parsing/actor_parsing/parse_[ARCHETYPE].py).

Each parser first uses BaseParser to retrieve some common data such as actor IDs, and then retrieves some data under a specific key such as car_data (in some cases, it's slightly more involved).

---

The DataHandler class is then responsible for combining actor data in a logical manner, and subsequently populating the resultant replay objects (see replay_objects/replay_object.py). This is meant to nicely yield the replay data for analysis, as it will be grouped in a manner that reflects our perception of the game (ie. a player is the same as their car, which in turn is the same as the sum of its components). The ActorParser calls DataHandler.create_dataframes() at the very end of the parsing process, which creates pandas.DataFrame objects for each replay object (this was used by carball for analysis).

The DataFormatter class just cleans-up the resultant pandas.DataFrame objects. Currently, it only removes the name column for each player, while recording all unique names directly to the player object.