Knokke, Flemish Region, Belgium
I am passionate about developing and implementing models, products and processes that enable professional football organizations to make better-informed decisions by leveraging insights from data. I have more than ten years of experience with building machine learning-based tools for player recruitment, match analysis and player development across academia, industry, and professional sports. I also have ample experience with building football data science teams from scratch and implementing evidence-based decision-making processes inside football organizations.
Leading the transition toward fully professional, evidence-based processes across the sports department, working closely with the Sports Director. Expanding the Football Data Science and Football Strategy & Insights teams while embedding data and structured decision-making into scouting, performance analysis, and broader football operations.
Drove the transition toward a more evidence-based organization by embedding football strategy into data, processes, and decision-making across scouting and performance analysis. - Translated the club's playing philosophy into a data-driven framework for evaluating and comparing players. - Redesigned scouting and match analysis processes to better integrate data and video analysis. - Led the development of internal tools to support scouting, recruitment, and player evaluation workflows. - Improved reporting structures to deliver more consistent and actionable insights to coaches and scouts. - Streamlined workflows across scouting and analysis teams, improving efficiency and collaboration. - Built and structured a Football Data Science team to support core analytical workflows.
Scaled analytics into structured systems, platforms, and team capabilities, enabling data-driven recruitment and decision-making at scale. - Developed a data-driven player evaluation and ranking system to support transfer decision-making. - Built data pipelines to identify and prioritize transfer targets based on player profiles and performance data. - Delivered automated reporting pipelines and live dashboards to support match analysis and performance monitoring. - Integrated data across multiple systems and providers to enable consistent and unified analysis. - Hired and coached analysts and technical profiles, building and developing the data team.
I am a scientific collaborator in the Machine Learning group at KU Leuven, where I am involved in several projects at the intersection of sports and machine learning.
I was a doctoral researcher in the Machine Learning group at KU Leuven, where I conducted research under the supervision of professor Jesse Davis. My research interests included transfer learning, statistical relational learning, probabilistic graphical models, sports-related predictions and sports analytics. My research was funded by the Flemish governmental agency for Innovation by Science and Technology (IWT), where I held a PhD Fellowship from January 2013 till December 2016.
I am a Data Editor in the FIFA Data Collection Group. I collect data about football players and football clubs in the Belgian Jupiler Pro League for EA Sports' annual FIFA football game.
I was in charge of the company's research and development team. - Coached four junior colleagues in the areas of machine learning and software engineering. - Investigated the possibilities of innovative technologies and products. - Developed a machine learning-based model to estimate the most likely transfer fees for professional football players based on data about their performances in matches. - Implemented an algorithm to determine the Governing Body Endorsement (GBE) status for male professional football players.
I was in charge of a team of data scientists and software engineers that was responsible for the development of a web application that enables professional football clubs to identify and assess potential transfer targets based on insights from data. - Re-designed the entire web application, improving its usability and look-and-feel. - Introduced novel data-driven metrics that identify the strengths and weaknesses as well as the playing styles of professional football players.
I was a machine learning researcher in the computer vision lab of professor Pascal Fua (CVLab). In close collaboration with the lab's spin-off company PlayfulVision, I developed an approach to identify recurring play patterns in volleyball match data.