Tom Decroos

Hi! I am Tom, I am a Sofware Engineer at Facebook and I used to be a PhD student in the DTAI Research group at KU Leuven working with Prof. Jesse Davis.

Download my resume.

Publications

For the most up-to-date list, see google scholar.

:star: PhD Thesis

:blue_book: Conferences

  1. SoccerMix: Representing Soccer Actions with Mixture Models Tom Decroos, Maaike Van Roy, Jesse Davis. ECML/PKDD 2020. paper slides
  2. Actions Speak Louder Than Goals: Valuing Player Actions in Soccer. Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis. KDD 2019 (Best Applied Data Science Paper). paper slides youtube
  3. Player Vectors: Characterizing Soccer Players’ Playing Style from Match Event Streams. Tom Decroos, Jesse Davis. ECML/PKDD 2019. paper slides
  4. Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Tom Decroos, Jan Van Haaren, Jesse Davis. KDD 2018. paper slides poster
  5. AMIE: Automatic Monitoring of Indoor Exercises. Tom Decroos, Kurt Schütte, Tim Op De Beéck, Benedicte Vanwanseele, Jesse Davis. ECML/PKDD 2018. paper slides poster
  6. Predicting Soccer Highlights from Spatio-Temporal Match Event Streams. Tom Decroos, Vladimir Dzyuba, Jan Van Haaren, Jesse Davis. AAAI 2017. paper poster

:orange_book: Workshops

  1. Interpretable Prediction of Goals in Soccer. Tom Decroos, Jesse Davis. Team Sports in AI workshop at AAAI 2020 / StatsBomb Innovation in Football Conference. paper slides youtube
  2. Valuing On-the-Ball Actions in Soccer: A Critical Comparison of xT and VAEP. Maaike Van Roy, Pieter Robberechts, Tom Decroos, Jesse Davis. Team Sports in AI workshop at AAAI 2020. paper slides.
  3. Analyzing Soccer Players’ Skill Ratings Over Time Using Tensor-Based Methods. Kenneth Verstraete, Tom Decroos, Bruno Coussement, Nick Vannieuwenhoven, Jesse Davis. Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2019 workshop. paper
  4. Characterizing Soccer Players’ Playing Style from Match Event Streams. Aron Geerts, Tom Decroos, Jesse Davis. Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2018 workshop. paper slides
  5. STARSS: A spatio-temporal action rating system for soccer. Tom Decroos, Jan Van Haaren, Vladimir Dzyuba, Jesse Davis. Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2017 workshop. paper slides
  6. Predicting the potential of professional soccer players. Ruben Vroonen, Tom Decroos, Jan Van Haaren, Jesse Davis. Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2017 workshop. paper slides

:newspaper: Media

Our work on valuing actions in soccer appeared in numerous newspapers and media outlets:

Software

I am the author and maintainer of two soccer-related pip packages:

  1. socceraction https://github.com/ML-KULeuven/socceraction: Convert soccer event stream data from commercial vendors (e.g., Opta, Wyscout, StatsBomb) to actions in the simpler SPADL language and value the actions using the VAEP framework.
  2. matplotsoccer https://github.com/TomDecroos/matplotsoccer: Visualize soccer event stream data and common figures such as heatmaps over a soccer field.

Community service

:mag: Reviewing

:computer: Organizing

I co-organized a three day Spring workshop on mining and learning (SMiLee) for the ML group of KU Leuven in 2017.

:school: Didactical tasks

I was a teaching assistant for the following courses:

I was a daily master’s thesis advisor for the following students:

I was the ombuds for both the Dutch Master Computerwetenschappen and the English Master in Computer Science at KU Leuven.

Contact

Email
<firstname>.<lastname>@gmail.com

You can also reach me via my LinkedIn or Twitter.

Last updated: 06/10/2020