| 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 | 311 | 2019 |
| 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 | 103 | 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 | 75 | 2017 |
| Player vectors: Characterizing soccer players’ playing style from match event streams T Decroos, J Davis Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020 | 42 | 2020 |
| Valuing on-the-ball actions in soccer: a critical comparison of XT and VAEP M Van Roy, P Robberechts, T Decroos, J Davis Proceedings of the 2020 AAAI Workshop on AI in Team Sports, 1-8, 2020 | 37 | 2020 |
| STARSS: A Spatio-Temporal Action Rating System for Soccer. T Decroos, J Van Haaren, V Dzyuba, J Davis MLSA@ PKDD/ECML, 11-20, 2017 | 37 | 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 |
| Predicting the Potential of Professional Soccer Players. R Vroonen, T Decroos, J Van Haaren, J Davis MLSA@ PKDD/ECML, 1-10, 2017 | 30 | 2017 |
| SoccerMix: Representing soccer actions with mixture models T Decroos, M Van Roy, J Davis Machine Learning and Knowledge Discovery in Databases. Applied Data Science …, 2021 | 22 | 2021 |
| AMIE: automatic monitoring of indoor exercises T Decroos, K Schütte, TO De Beéck, B Vanwanseele, J Davis Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019 | 18 | 2019 |
| Interpretable prediction of goals in soccer T Decroos, J Davis Proceedings of the AAAI-20 workshop on artificial intelligence in team sports, 2019 | 17 | 2019 |
| Soccer analytics meets artificial intelligence: learning value and style from soccer event stream data T Decroos Ph. D. Thesis, 2020 | 13 | 2020 |
| Analyzing soccer players’ skill ratings over time using tensor-based methods K Verstraete, T Decroos, B Coussement, N Vannieuwenhoven, J Davis Machine Learning and Knowledge Discovery in Databases: International …, 2020 | 7 | 2020 |
| Solving Euclidean Steiner tree problems with multi swarm optimization T Decroos, P De Causmaecker, B Demoen Proceedings of the Companion Publication of the 2015 Annual Conference on …, 2015 | 5 | 2015 |
| Analyzing Performance and Playing Style Using Ball Event Data J Van Haaren, P Robberechts, T Decroos, L Bransen, J Davis Football Analytics: Now and Beyond. A deep dive into the current state of …, 2019 | 4 | 2019 |
| Assessing the performances of soccer players J Davis, L Bransen, T Decroos, P Robberechts, J Van Haaren Proceedings of the 12th International Symposium on Computer Science in Sport …, 2020 | 3 | 2020 |
| How does context affect player performance in football? L Bransen, P Robberechts, J Davis, T Decroos, J Van Haaren Barça Innovation Hub, 2020 | | 2020 |
Jesse DavisProfessor, Department of Computer Science, KU LeuvenПотвърден имейл адрес: cs.kuleuven.be
