What Makes a Dribble Successful? Insights From 3D Pose Tracking Data
This work addresses the need for better performance evaluation in soccer analytics, specifically for dribbling skills, but it is incremental as it builds on existing tracking data methods.
The study tackled the problem of evaluating dribbling success in soccer by using 3D pose tracking data to capture aspects like balance and orientation, which are missed in traditional 2D data, and found that incorporating these features improved model performance.
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: one-on-one situations where an attacker attempts to bypass a defender with the ball. While previous research has primarily relied on 2D positional tracking data, this fails to capture aspects like balance, orientation, and ball control, limiting the depth of current insights. This study explores how pose tracking data (capturing players' posture and movement in three dimensions) can improve our understanding of dribbling skills. We extract novel pose-based features from 1,736 dribbles in the 2022/23 Champions League season and evaluate their impact on dribble success. Our results indicate that features capturing the attacker's balance and the alignment of the orientation between the attacker and defender are informative for predicting dribble success. Incorporating these pose-based features on top of features derived from traditional 2D positional data leads to a measurable improvement in model performance.