| 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 | 300 | 2019 |
| 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 | 99 | 2012 |
| 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 | 98 | 2018 |
| 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 | 74 | 2017 |
| Lifted generative learning of Markov logic networks J Van Haaren, G Van den Broeck, W Meert, J Davis Machine Learning 103, 27-55, 2016 | 50 | 2016 |
| 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 | 47 | 2019 |
| 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 | 44 | 2019 |
| 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 | 43 | 2019 |
| 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 | 39 | 2015 |
| STARSS: A Spatio-Temporal Action Rating System for Soccer. T Decroos, J Van Haaren, V Dzyuba, J Davis MLSA@ PKDD/ECML, 11-20, 2017 | 36 | 2017 |
| 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 | 34 | 2020 |
| TODTLER: Two-Order-Deep Transfer Learning J Van Haaren, A Kolobov, J Davis Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence …, 2015 | 34 | 2015 |
| 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 | 33 | 2019 |
| Qualitative spatial reasoning for soccer pass prediction V Vercruyssen, L De Raedt, J Davis CEUR Workshop Proceedings 1842, 2016 | 33 | 2016 |
| 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 | 33 | 2016 |
| Player chemistry: Striving for a perfectly balanced soccer team L Bransen, J Van Haaren arXiv preprint arXiv:2003.01712, 2020 | 29 | 2020 |
| Predicting the Potential of Professional Soccer Players. R Vroonen, T Decroos, J Van Haaren, J Davis MLSA@ PKDD/ECML, 1-10, 2017 | 28 | 2017 |
| Relational learning for football-related predictions J Van Haaren, G Van den Broeck Latest advances in inductive logic programming, 237-244, 2015 | 28 | 2015 |
| 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 | 27 | 2021 |
| Machine learning and data mining for sports analytics U Brefeld, J Davis, J Van Haaren, A Zimmermann Cham: Springer, 2018 | 27 | 2018 |
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
