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 and subgroup discovery.
Original language | English |
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Title of host publication | Machine Learning and Knowledge Discovery in Databases |
Subtitle of host publication | European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III |
Publisher | Springer |
Pages | 786-789 |
ISBN (Electronic) | 978-3-030-46133-1 |
ISBN (Print) | 978-3-030-46132-4 |
DOIs | |
Publication status | Published - 30 Apr 2020 |
Event | European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019 - Hubland campus University of Würzburg, Würzburg, Germany Duration: 16 Sep 2019 → 20 Sep 2019 https://ecmlpkdd2019.org/ |
Publication series
Name | Lecture notes in computer science |
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Volume | 11908 |
Conference
Conference | European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019 |
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Abbreviated title | ECML-PKDD 2019 |
Country | Germany |
City | Würzburg |
Period | 16/09/19 → 20/09/19 |
Internet address |