activity
20192025
most citedPredicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

18 citations · 48 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2025

What Makes a Dribble Successful? Insights From 3D Pose Tracking Data

Michiel Schepers, Pieter Robberechts, Jan Van Haaren +1

Data analysis plays an increasingly important role in soccer, offering new ways to evaluate individual and team performance. One specific application is the evaluation of dribbles:…

cs.LG2025

Warping and Matching Subsequences Between Time Series

Simiao Lin, Wannes Meert, Pieter Robberechts +1

Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparis…

cs.LG2024★ 2 cited

Biases in Expected Goals Models Confound Finishing Ability

Jesse Davis, Pieter Robberechts

Expected Goals (xG) has emerged as a popular tool for evaluating finishing skill in soccer analytics. It involves comparing a player's cumulative xG with their actual goal output,…

cs.LG2022

Elastic Product Quantization for Time Series

Pieter Robberechts, Wannes Meert, Jesse Davis

Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate c…

cs.AI2021★ 10 cited

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang +2

Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of lo…

cs.LG2019★ 18 cited

Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

Pieter Robberechts, Rud Derie, Pieter Van den Berghe +4

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods e…

cs.LG2019★ 18 cited

A Bayesian Approach to In-Game Win Probability in Soccer

Pieter Robberechts, Jan Van Haaren, Jesse Davis

In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In…