sertalpbilal/rocket_league_analytics

Fun with Rocket League tracking data

Rocket League Analytics

Rocket League detailed stat dashboard with expected goals analysis.

See dashboard here: https://sertalpbilal.github.io/rocket_league_analytics

Generating xG model and dashboard

The xG model file is under src/xg_model. The model depends on having a valid key from ballchasing.com API.

Steps:

1. Setting up environment
   Add your BallChasing token to .env.sample file and rename it to .env
2. Collecting random game data (optional)
   Navigate to src/scripts and run python collect_model_games.py
   This step will download latest 1000 ranked double games under data/model directory.
3. Generating xG model (optional)
   Run python generate_xg.py to populate xG model.
   Note that you might need to run this inside a Docker container if you cannot install carball package.
   This step will populate two files under data/model: xg.model (gradient boosting xG model) and xg.scaler (feature scaler)
4. Download your games
   You can go to scripts folder and run python download_my_games.py
   This step will download your games to data/replay and will populate data/json and data/dataframe directories for future use.
5. Generate the dashboard
   Finally you can go to src and run python boxcartest.py to populate the dashboard.

Screenshots:

Considering all games

Click here to view in an alternative format

Considering the latest streak of games

Click here to view in an alternative format

Output:

Alongside all of the charts produced by the program, the program also outputs 11 tables.

1. 
2. 
3. 
4. 
5. 
6. 
7. 
8. 
9. 
10. 
11. 

Tables for the latest streak of matches can be found here.