Convert soccer event stream data to the SPADL format
and value on-the-ball player actions
This is a Python package for objectively quantifying the impact of the individual actions performed by soccer players using event stream data. It contains the following components:
- Converters for event stream data to the SPADL and atomic-SPADL formats, which are unified and expressive languages for on-the-ball player actions. Read more »
- An implementation of the VAEP and Atomic-VAEP frameworks to value actions on their expected impact on the score line. Read more »
- An implementation of the xT framework to value ball-progressing actions using a possession-based Markov model. Read more »
The recommended way to install socceraction is to simply use pip:
$ pip install socceractionsocceraction officially supports Python 3.7--3.9.
The folder public-notebooks provides a demo of the full pipeline from raw StatsBomb data to action values and player ratings. More detailed installation/usage instructions can be found in the documentation.
For more information about SPADL and VAEP, read our SIGKDD paper "Actions Speak Louder Than Goals: Valuing Player Actions in Soccer" available on ACM (https://dl.acm.org/citation.cfm?doid=3292500.3330758) and Arxiv (https://arxiv.org/abs/1802.07127).
For more information about xT, read Karun Singh's blog post: https://karun.in/blog/expected-threat.html
If you make use of this package or the ideas in our paper, please use the following citation:
@inproceedings{Decroos2019actions,
author = {Decroos, Tom and Bransen, Lotte and Van Haaren, Jan and Davis, Jesse},
title = {Actions Speak Louder Than Goals: Valuing Player Actions in Soccer},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
series = {KDD '19},
year = {2019},
isbn = {978-1-4503-6201-6},
location = {Anchorage, AK, USA},
pages = {1851--1861},
numpages = {11},
url = {http://doi.acm.org/10.1145/3292500.3330758},
doi = {10.1145/3292500.3330758},
acmid = {3330758},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {event stream data, probabilistic classification, soccer match data, sports analytics, valuing actions},
}