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Jan Van Haaren
Jan Van Haaren
Club Brugge and KU Leuven
Geverifieerd e-mailadres voor cs.kuleuven.be - Homepage
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Actions speak louder than goals: Valuing player actions in soccer
T Decroos, L Bransen, J Van Haaren, J Davis
Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019
3112019
Automatic discovery of tactics in spatio-temporal soccer match data
T Decroos, J Van Haaren, J Davis
Proceedings of the 24th acm sigkdd international conference on knowledge …, 2018
1032018
Markov network structure learning: A randomized feature generation approach
J Van Haaren, J Davis
Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1148-1154, 2012
992012
Predicting soccer highlights from spatio-temporal match event streams
T Decroos, V Dzyuba, J Van Haaren, J Davis
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
752017
Lifted generative learning of Markov logic networks
J Van Haaren, G Van den Broeck, W Meert, J Davis
Machine Learning 103, 27-55, 2016
512016
Measuring football players’ on-the-ball contributions from passes during games
L Bransen, J Van Haaren
Machine Learning and Data Mining for Sports Analytics: 5th International …, 2019
502019
Measuring soccer players’ contributions to chance creation by valuing their passes
L Bransen, J Van Haaren, M van de Velden
Journal of Quantitative Analysis in Sports 15 (2), 97-116, 2019
452019
Choke or shine? Quantifying soccer players’ abilities to perform under mental pressure
L Bransen, P Robberechts, J Van Haaren, J Davis
Proceedings of the 13th MIT sloan sports analytics conference, 1-25, 2019
452019
Automatically discovering offensive patterns in soccer match data
J Van Haaren, V Dzyuba, S Hannosset, J Davis
Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015
392015
STARSS: A Spatio-Temporal Action Rating System for Soccer.
T Decroos, J Van Haaren, V Dzyuba, J Davis
MLSA@ PKDD/ECML, 11-20, 2017
372017
VAEP: An objective approach to valuing on-the-ball actions in soccer
T Decroos, L Bransen, J Van Haaren, J Davis
IJCAI, 4696-4700, 2020
342020
Distinguishing between roles of football players in play-by-play match event data
B Aalbers, J Van Haaren
Machine Learning and Data Mining for Sports Analytics: 5th International …, 2019
342019
Analyzing volleyball match data from the 2014 world championships using machine learning techniques
J Van Haaren, H Ben Shitrit, J Davis, P Fua
Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016
342016
TODTLER: Two-Order-Deep Transfer Learning
J Van Haaren, A Kolobov, J Davis
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence …, 2015
342015
Qualitative Spatial Reasoning for Soccer Pass Prediction.
V Vercruyssen, L De Raedt, J Davis
MLSA@ PKDD/ECML, 2016
332016
Predicting the Potential of Professional Soccer Players.
R Vroonen, T Decroos, J Van Haaren, J Davis
MLSA@ PKDD/ECML, 1-10, 2017
302017
A bayesian approach to in-game win probability in soccer
P Robberechts, J Van Haaren, J Davis
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
292021
Player chemistry: Striving for a perfectly balanced soccer team
L Bransen, J Van Haaren
arXiv preprint arXiv:2003.01712, 2020
292020
Relational learning for football-related predictions
J Van Haaren, G Van den Broeck
Latest advances in inductive logic programming, 237-244, 2015
282015
Machine learning and data mining for sports analytics
U Brefeld, J Davis, J Van Haaren, A Zimmermann
Cham: Springer, 2018
272018
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Artikelen 1–20