Publications

This page shows a selection of my sports analytics publications. Lirias and Google Scholar provide a complete overview of my publications.

• In Professional Football the Decline in High-Intensity Running Activities from First to Second Half Is More Pronounced in Players with a Fast Muscle Typology. Freek Van de Casteele, Dieter Deprez, Jan Van Haaren, Wim Derave and Eline Lievens. Scandinavian Journal of Medicine & Science in Sports. October 2023. Paper.
• Towards Maximizing Expected Possession Outcome in Soccer. Pegah Rahimian, Jan Van Haaren and László Toka. International Journal of Sports Science & Coaching. February 2023. Paper.
• Evaluating Sports Analytics Models: Challenges, Approaches, and Lessons Learned. Jesse Davis, Lotte Bransen, Laurens Devos, Wannes Meert, Pieter Robberechts, Jan Van Haaren and Maaike Van Roy. Workshop on AI Evaluation Beyond Metrics (EBeM 2022). July 2022. Paper.
• Beyond Action Valuation: A Deep Reinforcement Learning Framework for Optimizing Player Decisions in Soccer. Pegah Rahimian, Jan Van Haaren, Togzhan Abzhanova and László Toka. Proceedings of the 16th MIT Sloan Sports Analytics Conference. March 2022. Paper.
• Why Would I Trust Your Numbers? On the Explainability of Expected Values in Soccer. Jan Van Haaren. Workshop on Artificial Intelligence for Sports Analytics (AISA 2021). August 2021. Paper.
• A Bayesian Approach to In-Game Win Probability in Soccer. Pieter Robberechts, Jan Van Haaren and Jesse Davis. Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). August 2021. Paper.
• How Does Context Affect Player Performance in Football?. Lotte Bransen, Pieter Robberechts, Jesse Davis, Tom Decroos and Jan Van Haaren. Football Analytics 2021: The Role of Context in Transferring Analytics to the Pitch. November 2020. Paper.
• VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer. Tom Decroos, Lotte Bransen, Jan Van Haaren and Jesse Davis. Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020). July 2020. Paper. Code.
• Player Chemistry: Striving for a Perfectly Balanced Soccer Team. Lotte Bransen and Jan Van Haaren. Proceedings of the 14th MIT Sloan Sports Analytics Conference. March 2020. Paper.
• Analyzing Performance and Playing Style Using Ball Event Data. Jan Van Haaren, Pieter Robberechts, Tom Decroos, Lotte Bransen and Jesse Davis. Football Analytics: Now and Beyond. A Deep Dive into the Current State of Advanced Data Analytics. November 2019. Paper.
• Actions Speak Louder Than Goals: Valuing Player Actions in Soccer. Tom Decroos, Lotte Bransen, Jan Van Haaren and Jesse Davis. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019). August 2019. Paper. Code.
• Choke or Shine? Quantifying Soccer Players’ Abilities to Perform Under Mental Pressure. Lotte Bransen, Pieter Robberechts, Jan Van Haaren and Jesse Davis. Proceedings of the 13th MIT Sloan Sports Analytics Conference. March 2019. Paper.
• Measuring Soccer Players’ Contributions to Chance Creation by Valuing Their Passes. Lotte Bransen, Jan Van Haaren and Michel van de Velden. Journal of Quantitative Analysis in Sports. January 2019. Paper.
• Measuring Football Players’ On-the-Ball Contributions from Passes During Games. Lotte Bransen and Jan Van Haaren. Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2018). September 2018. Paper.
• Distinguishing Between Roles of Football Players in Play-by-Play Match Event Data. Bart Aalbers and Jan Van Haaren. Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2018). September 2018. Paper.
• Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Tom Decroos, Jan Van Haaren and Jesse Davis. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018). August 2018. Paper.
• STARSS: A Spatio-Temporal Action Rating System for Soccer. Tom Decroos, Jan Van Haaren, Vladimir Dzyuba and Jesse Davis. Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2017). September 2017. Paper.
• Predicting the Potential of Professional Soccer Players. Ruben Vroonen, Tom Decroos, Jan Van Haaren and Jesse Davis. Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2017). September 2017. Paper.
• Predicting Soccer Highlights from Spatio-Temporal Match Event Streams. Tom Decroos, Vladimir Dzyuba, Jan Van Haaren and Jesse Davis. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017). February 2017. Paper.
• Strategy Discovery in Professional Soccer Match Data. Jan Van Haaren, Siebe Hannosset and Jesse Davis. Workshop on Large-Scale Sports Analytics (LSSA 2016). August 2016. Paper.
• Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques. Jan Van Haaren, Horesh Ben Shitrit, Jesse Davis and Pascal Fua. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016). August 2016. Paper.
• Predicting the Final League Tables of Domestic Football Leagues. Jan Van Haaren and Jesse Davis. International Conference on Mathematics in Sport (MathSport International 2015). July 2015. Paper.
• Automatically Discovering Offensive Patterns in Soccer Match Data. Jan Van Haaren, Vladimir Dzyuba, Siebe Hannosset and Jesse Davis. International Symposium on Intelligent Data Analysis (IDA 2015). June 2015. Paper.