/Euroleague-ML

The aim of this repository is to publish several Python Notebooks to see how Machine Learning can be applied to basketball-based European Datasets.

Primary LanguageJupyter Notebook

Euroleague-ML

The aim of this repository is to publish several Python Notebooks to see how Machine Learning (ML) can be applied to basketball-based European Datasets. Although the given examples are quite simple (build-in scikit-learn functions), I expect these models to provide coaches/analysts/GM's with concrete baseline examples. In particular, at the moment (January 12th 2020), three tutorials about ML gold-standards have been included:

  1. Clustering, where K-Means is used to group similar types of shooters based on shotchart-based features.
  2. Classification, where SVM is used to build a model able to classify player positions.
  3. Regression, where linear models are used in order to predict several statistics, as well as suggesting potentially interesting players for a given lineup.

I really hope you find it useful. If you have any questions / suggestions, feel free to send me an email (adria.arbues@upf.edu) or a Twitter DM (@arbues6). Big shoutout also to Nacho Gámez (@ngamezj), who provided me with a notable dataset to get started.

Required Dependencies

All code has been written in Python (3.7); the following libraries should be installed (all of the are included in pip3):

pip3 install numpy
pip3 install opencv-python
pip3 install pandas
pip3 install matplotlib
pip3 install scikit-learn

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