UnFOOT: Unsupervised Football Analytics Tool
Abstract
Labelled football (soccer) data is hard to acquire and it usually needs humans to annotate the match events. This process makes it more expensive to be obtained by smaller clubs. UnFOOT (Unsupervised Football Analytics Tool) combines data mining techniques and basic statistics to measure the performance of players and teams from positional data. The capabilities of the tool involve preprocessing the match data, extraction of features, visualization of player and team performance. It also has built-in data mining techniques, such as association rule mining, subgroup discovery and a proposed approach to look for frequent distributions.
Cite
Text
Coutinho et al. "UnFOOT: Unsupervised Football Analytics Tool." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_52Markdown
[Coutinho et al. "UnFOOT: Unsupervised Football Analytics Tool." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/coutinho2019ecmlpkdd-unfoot/) doi:10.1007/978-3-030-46133-1_52BibTeX
@inproceedings{coutinho2019ecmlpkdd-unfoot,
title = {{UnFOOT: Unsupervised Football Analytics Tool}},
author = {Coutinho, José Carlos and Mendes-Moreira, João and de Sá, Cláudio Rebelo},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {786-789},
doi = {10.1007/978-3-030-46133-1_52},
url = {https://mlanthology.org/ecmlpkdd/2019/coutinho2019ecmlpkdd-unfoot/}
}