unravelsports 1.2.1
A project to analyze sports event and tracking data
Requires Python: >=3.11

![unravelsports logo](https://github.com/UnravelSports/unravelsports.github.io/blob/main/imgs/unravelsports-5500x800.png?raw=true)

<div align="right">

[![UnravelSports](https://img.shields.io/badge/powered%20by-UnravelSports-orange.svg?style=flat&colorB=E6B611&colorA=C3C3C3)](https://unravelsports.github.io/)
[![tl;dr legal](https://img.shields.io/badge/license-Mozilla%20Public%20License%20v2.0-orange.svg?style=flat&colorA=C3C3C3&colorB=E20E6A)](https://www.tldrlegal.com/license/mozilla-public-license-2-0-mpl-2)

</div>

🌀 `pip install unravelsports`

🌀 Documentation
----------------

Finally, we have documentation! Check out:

[
    👉 unravelsports.readthedocs.io](https://unravelsports.readthedocs.io "https://unravelsports.readthedocs.io")

🌀 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**](#polars-dataframes)
- ⚽ 🏈 [**Graph Neural Network**](#graph-neural-networks) Training, Graph Conversion and Prediction [[🔗 Bekkers &amp; Sahasrabudhe (2023)](https://arxiv.org/pdf/2411.17450)]
- ⚽ [**Pressing Intensity**](#pressing-intensity) [[🔗 Bekkers (2024)](https://arxiv.org/pdf/2501.04712)]
- ⚽ [**Formation and Position Identification (EFPI)**](#formation-and-position-identification) [[🔗 Bekkers (2025)](https://arxiv.org/pdf/2506.23843)]

🌀 Features
-----------

### **Polars DataFrames**

⚽🏈 **Convert Tracking Data** into [Polars DataFrames](https://pola.rs/) for rapid data conversion and data processing.

⚽ For soccer we rely on [Kloppy](https://kloppy.pysport.org/) and as such we support Sportec, SkillCorner, PFF / GradientSports, Metrica, StatsPerform, Tracab (CyronHego), SecondSpectrum, HawkEye and Signality tracking data.

```python
from unravel.soccer import KloppyPolarsDataset

from kloppy import sportec

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

|   | period_id | timestamp       | frame_id | ball_state | id             |      x |     y | z | team_id        | position_name | game_id        |     vx |     vy | vz |     v | ax | ay | az | a | ball_owning_team_id | is_ball_carrier |
| -: | --------: | :-------------- | -------: | :--------- | :------------- | -----: | ----: | -: | :------------- | :------------ | :------------- | -----: | -----: | -: | ----: | -: | -: | -: | -: | :------------------ | :-------------- |
| 0 |         1 | 0 days 00:00:00 |    10000 | alive      | DFL-OBJ-00008F | -20.67 | -4.56 | 0 | DFL-CLU-000005 | RCB           | DFL-MAT-J03WPY |  0.393 | -0.214 |  0 | 0.447 |  0 |  0 |  0 | 0 | DFL-CLU-00000P      | False           |
| 1 |         1 | 0 days 00:00:00 |    10000 | alive      | DFL-OBJ-0000EJ |  -8.86 | -0.94 | 0 | DFL-CLU-000005 | UNK           | DFL-MAT-J03WPY | -0.009 |  0.018 |  0 |  0.02 |  0 |  0 |  0 | 0 | DFL-CLU-00000P      | False           |
| 2 |         1 | 0 days 00:00:00 |    10000 | alive      | DFL-OBJ-0000F8 |  -2.12 |  9.85 | 0 | DFL-CLU-00000P | RM            | DFL-MAT-J03WPY |      0 |      0 |  0 |     0 |  0 |  0 |  0 | 0 | DFL-CLU-00000P      | False           |
| 3 |         1 | 0 days 00:00:00 |    10000 | alive      | DFL-OBJ-0000NZ |   0.57 | 23.23 | 0 | DFL-CLU-00000P | RB            | DFL-MAT-J03WPY |  0.179 | -0.134 |  0 | 0.223 |  0 |  0 |  0 | 0 | DFL-CLU-00000P      | False           |
| 4 |         1 | 0 days 00:00:00 |    10000 | alive      | DFL-OBJ-0001HW | -46.26 |  0.08 | 0 | DFL-CLU-000005 | GK            | DFL-MAT-J03WPY |  0.357 |  0.071 |  0 | 0.364 |  0 |  0 |  0 | 0 | DFL-CLU-00000P      | False           |

🏈 For American Football we use [BigDataBowl Data](https://www.kaggle.com/competitions/nfl-big-data-bowl-2025/data) directly.

```python
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](#polars-dataframes)** into [Graphs](examples/graphs_faq.md) to train **graph neural networks**. These [Graphs](examples/graphs_faq.md) can be used with [**PyTorch Geometric**](https://pytorch-geometric.readthedocs.io/en/latest/) or [**Spektral**](https://github.com/danielegrattarola/spektral).
`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.

```python
from unravel.soccer import SoccerGraphConverter

converter = SoccerGraphConverter(
    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**](https://arxiv.org/abs/2501.04712) for a whole game (or segment) of Soccer tracking data.

See [**Pressing Intensity Jupyter Notebook**](examples/pressing_intensity.ipynb) for an example how to create mp4 videos.

```python
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,
) 
```

![1. FC Köln vs. FC Bayern München (May 27th 2023)](assets/gif/preview.gif)

---

### **Formation and Position Identification**

Compute [Elastic Formation and Position Identification, **EFPI**](https://arxiv.org/pdf/2506.23843) for individual frames, possessions, periods or specific time intervals for Soccer.

For more information on all possibilities for "every" check out [Polars Documentation](https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.group_by_dynamic.html).

```python
from unravel.soccer import EFPI

model = EFPI(dataset=kloppy_polars_dataset)
model.fit(
    # Default 65 formations , or specify a subset (e.g. ["442" , "433"])
    formations=None,
    # specific time intervals (e.g. 1m, 1m14s, 2m30s etc.), or specify "possession", "period" or "frame".
    every="5m",
    substitutions="drop",
    change_threshold=0.1,
    change_after_possession=True,
)
```

![Elastic Formation and Position Identification Example](assets/efpi.png)

⌛ ***More to come soon...!***

🌀 Quick Start
--------------

📖 ⚽ The [**Quick Start Jupyter Notebook**](examples/0_quick_start_guide_pyg.ipynb) 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**](examples/1_kloppy_gnn_train_pyg.ipynb) gives an in-depth walkthrough.

📖 🏈 The [**BigDataBowl Converter Tutorial Jupyter Notebook**](examples/2_big_data_bowl_guide.ipynb) gives an guide on how to convert the BigDataBowl data into Graphs.

📖 ⚽ The [**Pressing Intensity Tutorial Jupyter Notebook**](examples/pressing_intensity.ipynb) gives a description on how to create Pressing Intensity videos.

🌀 Additional Reading
---------------------

📖 [A Graph Neural Network Deep-dive into Successful Counterattacks {A. Sahasrabudhe  J. Bekkers, 2023}](https://github.com/USSoccerFederation/ussf_ssac_23_soccer_gnn/tree/main)

🎤 [**Cutting Edge Football Analytics:** _using `polars`, `keras` and `spektral`_ (PyData London, 2025)](https://rawcdn.githack.com/UnravelSports/keynotes/main/html/20250607-PyData-London.html)

🌀 Installation
---------------

The easiest way to get started is:

```bash
pip install unravelsports
```

🌀 Licenses
-----------

This project is licensed under the [Mozilla Public License Version 2.0 (MPL)](LICENSE), 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.

🌀 Citation
-----------

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

📎 [The `unravelsports` package](https://github.com/unravelsports/unravelsports).

<details>
<summary>BibTex</summary>
<pre>
@software{unravelsports2024repository,
  author = {Bekkers, Joris},
  title = {unravelsports},
  version = {2.0.0},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/unravelsports/unravelsports}
}
</pre>
</details>

<br>

📎 [Bekkers, J., &amp; Sahasrabudhe, A. (2024). A Graph Neural Network deep-dive into successful counterattacks. arXiv preprint arXiv:2411.17450.](https://arxiv.org/pdf/2411.17450)

<details>
<summary>BibTex</summary>
<pre>
@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}
}
</pre>
</details>
<br>

📎 [Bekkers, J. (2024). Pressing Intensity: An Intuitive Measure for Pressing in Soccer. arXiv preprint arXiv:2501.04712.](https://arxiv.org/pdf/2501.04712)

<details>
<summary>BibTex</summary>
<pre>
@article{bekkers2024pressing,
  title={Pressing Intensity: An Intuitive Measure for Pressing in Soccer},
  author={Bekkers, Joris},
  journal={arXiv preprint arXiv:2501.04712},
  year={2024}
}
</pre>
</details>
<br>

📎 [Bekkers, J. (2025). EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment. arXiv preprint arXiv:2506.23843.](https://arxiv.org/pdf/2506.23843)

<details>
<summary>BibTex</summary>
<pre>
@article{bekkers2025efpi,
  title={EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment},
  author={Bekkers, Joris},
  journal={arXiv preprint arXiv:2506.23843},
  year={2025}
}
</pre>
</details>
<br>

🌀 Social Media
---------------

[`<img alt="LinkedIn" width="40px" src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png"/>`](https://www.linkedin.com/in/joris-bekkers-33138288/)
[`<img alt="Bluesky" width="40px" src="https://upload.wikimedia.org/wikipedia/commons/thumb/7/7a/Bluesky_Logo.svg/2319px-Bluesky_Logo.svg.png"/>`](https://bsky.app/profile/unravelsports.com)