unravelsports 0.4.0

🌀 pip install unravelsports

🌀 What is it?

The unravelsports package aims to aid researchers, analysts and enthusiasts by providing intermediary steps in the complex process of converting raw sports data into meaningful information and actionable insights.

This package currently supports:

• ⚽ 🏈 Polars DataFrame Conversion
• ⚽ 🏈 Graph Neural Network Training, Graph Conversion and Prediction [Bekkers & Sahasrabudhe (2023)]
• ⚽ Pressing Intensity [Bekkers (2024)]

🌀 Features

Polars DataFrames

⚽🏈 Convert Tracking Data into Polars DataFrames for rapid data conversion and data processing.

⚽ For soccer we rely on Kloppy and as such we support Sportec$^1$, SkillCorner$^1$, PFF$^{1, 2}$, Metrica$^1$, StatsPerform, Tracab (CyronHego) and SecondSpectrum tracking data.

from unravel.soccer import KloppyPolarsDataset

from kloppy import sportec

kloppy_dataset = sportec.load_open_tracking_data()
kloppy_polars_dataset = KloppyPolarsDataset(
    kloppy_dataset=kloppy_dataset
)

$^1$ Open data available through kloppy.

$^2$ Currently unreleased in kloppy, only available through kloppy master branch. Click here for World Cup 2022 Dataset

🏈 For American Football we use BigDataBowl Data directly.

from unravel.american_football import BigDataBowlDataset

bdb = BigDataBowlDataset(
    tracking_file_path="week1.csv",
    players_file_path="players.csv",
    plays_file_path="plays.csv",
)

Graph Neural Networks

⚽🏈 Convert Polars Dataframes into Graphs to train graph neural networks. These Graphs can be used with Spektral - a flexible framework for training graph neural networks. unravelsports allows you to randomize and split data into train, test and validation sets along matches, sequences or possessions to avoid leakage and improve model quality. And finally, train, validate and test your (custom) Graph model(s) and easily predict on new data.

converter = SoccerGraphConverterPolars(
    dataset=kloppy_polars_dataset,
    self_loop_ball=True,
    adjacency_matrix_connect_type="ball",
    adjacency_matrix_type="split_by_team",
    label_type="binary",
    defending_team_node_value=0.1,
    non_potential_receiver_node_value=0.1,
    random_seed=False,
    pad=False,
    verbose=False,
)

Pressing Intensity

Compute Pressing Intensity for a whole game (or segment) of Soccer tracking data.

See Pressing Intensity Jupyter Notebook for an example how to create mp4 videos.

from unravel.soccer import PressingIntensity

import polars as pl

model = PressingIntensity(
    dataset=kloppy_polars_dataset
)
model.fit(
    start_time = pl.duration(minutes=1, seconds=53),
    end_time = pl.duration(minutes=2, seconds=32),
    period_id = 1,
    method="teams",
    ball_method="max",
    orient="home_away",
    speed_threshold=2.0,
) 

⌛ More to come soon...!

🌀 Quick Start

📖 ⚽ The Quick Start Jupyter Notebook explains how to convert any positional tracking data from Kloppy to Spektral GNN in a few easy steps while walking you through the most important features and documentation.

📖 ⚽ The Graph Converter Tutorial Jupyter Notebook gives an in-depth walkthrough.

📖 🏈 The BigDataBowl Converter Tutorial Jupyter Notebook gives an guide on how to convert the BigDataBowl data into Graphs.

📖 ⚽ The Pressing Intensity Tutorial Jupyter Notebook gives a description on how to create Pressing Intensity videos.

🌀 Documentation

For now, follow the Graph Converter Tutorial and check the Graph FAQ, more documentation will follow!

Additional reading:

📖 A Graph Neural Network Deep-dive into Successful Counterattacks {A. Sahasrabudhe & J. Bekkers, 2023}

🌀 Installation

The easiest way to get started is:

pip install unravelsports

⚠️ Due to compatibility issues unravelsports currently only works on Python 3.11 with:

spektral==1.20.0 
tensorflow==2.14.0 
keras==2.14.0
kloppy==3.16.0
polars==1.2.1

These dependencies come pre-installed with the package. It is advised to create a virtual environment.

This package is tested on the latest versions of Ubuntu, MacOS and Windows.

🌀 Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

An overview on how to contribute can be found in the contributing guide.

I try to keep an up-to-date list of Open Issues. If you would like to work on any of these, or have ideas of your own that could be added, send me a message on LinkedIn or BlueSky

🌀 Citation

If you use this repository for any educational purposes, research, project etc., please reference both:

📎 The unravelsports package.

@software{unravelsports2024repository,
  author = {Bekkers, Joris},
  title = {unravelsports},
  version = {0.3.0},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/unravelsports/unravelsports}
}

📎 Bekkers, J., & Sahasrabudhe, A. (2024). A Graph Neural Network deep-dive into successful counterattacks. arXiv preprint arXiv:2411.17450.

@inproceedings{sahasrabudhe2023graph,
  title={A Graph Neural Network deep-dive into successful counterattacks},
  author={Sahasrabudhe, Amod and Bekkers, Joris},
  booktitle={17th Annual MIT Sloan Sports Analytics Conference. Boston, MA, USA: MIT},
  pages={15},
  year={2023}
}

📎 Bekkers, J. (2024). Pressing Intensity: An Intuitive Measure for Pressing in Soccer. arXiv preprint arXiv:2501.04712.

@article{bekkers2024pressing,
  title={Pressing Intensity: An Intuitive Measure for Pressing in Soccer},
  author={Bekkers, Joris},
  journal={arXiv preprint arXiv:2501.04712},
  year={2024}
}

🌀 Social Media

🌀 Licenses

This project is licensed under the Mozilla Public License Version 2.0 (MPL), which requires that you include a copy of the license and provide attribution to the original authors. Any modifications you make to the MPL-licensed files must be documented, and the source code for those modifications must be made open-source under the same license.