StatsBomb data

This repository assumes that the StatsBomb open-data has already been cloned to a local directory.

To run the notebooks

All of the notebooks can be run from an Anaconda environment.

To install the environment yourself use the Anaconda Prompt. Run the following command from the expected-goals directory:

conda env create -f environment.yml

Activate the new environment from the prompt:

conda activate expected-goals

Load Jupyter Notebook from the prompt:

jupyter notebook

To run the analysis you need to first create the datasets. By running the notebooks in the notebooks/create-data directory (in numerical order):

00_statsbomb_data_to_parquet.ipynb: creates the StatsBomb data in the data/statsbomb folder
01_wyscout_data_to_parquet.ipynb: creates the Wyscout data in the data/wyscout folder
02_remove_overlap_wyscout_statsbomb.ipynb: removes 100 overlapping games from the Wyscout data
03_wyscout_shot_dataset.ipynb: creates a Wyscout shot dataset: data/wyscout/shots.parquet
04_statsbomb_freeze_frame_features.ipynb: creates some features from the StatsBomb freeze-frame data: data/statsbomb/freeze_features.parquet
05_statsbomb_shot_dataset.ipynb: creates a StatsBomb shot dataset: data/statsbomb/shots.parquet
06_combine_shots_dataset.ipynb: creates an overall shot dataset: data/shots.parquet
07_add_synthetic_shots_remove_outliers.ipynb: removes 227 outliers from the data/shots.parquet generates 1000 fake shots (data/fake_shots.parquet)

00-explore-data-quality-overlap.ipynb: explores the 100 overlapping Wyscout/StatsBomb games and their data quality
01-expected-goals-model.ipynb: builds two expected goals models: logistic regression and light gradient boosting machines
02-expected-goals-calculate-xg-and-shap.ipynb: calculates xG and shapely values (contributions of the features to the probability of a goal)
03-visualize-models.ipynb: visualize the model using non-negative matrix factorisation, partial dependence plots, and Shapely values
04-kernel-density-probability-scoring.ipynb: a basic model of shot quality by location using kernel density estimators
05-simulate-match-results-from-xg.ipynb: simulate league tables using expected goals
06-freeze_frame-example.ipynb: a plot a StatsBomb freeze frame
07-red-zone-heatmap.ipynb: heatmaps for the goal scoring probabilities
08-shots_follow_poisson_distribution.ipynb: a bar chart to show that goals per game can be approximated by a Poisson distribution
09_figure3_angle_features.ipynb: a figure to show how the angles for expected goals models are calculated