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

TL;DR

This paper demonstrates that 3D pose tracking data, capturing players' posture and movement, enhances the prediction of dribble success in soccer beyond traditional 2D positional data.

Contribution

It introduces novel pose-based features from 3D tracking data and shows their effectiveness in improving dribble success prediction models.

Findings

Pose-based features improve prediction accuracy.

Balance and orientation alignment are key indicators.

3D data provides deeper insights than 2D tracking.

Abstract

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.