Less is More: End-to-end Learning of Insights from a Single Motion
Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics
Abstract
On-field sensor-based soccer player tracking solutions are emerging and
provide new insights into the dynamics of the player during training or
a match. Yet, not all player positions are equally privileged.
Goalkeepers’ training and performance assess- ment were for a long time
ignored. Understanding what is ”the side of the post where most high
dives were performed” provides valuable input for both the trainer and
the athlete to improve perfor- mance or avoid injuries. In the current
study, we focus on a practical methodology to extract insights from
goalkeeper kinematics to inform such analytics. We demonstrate that
information from a single motion sensor can be successfully used for
learning patterns in goalkeeper’s motion and provide an explainable
goalkeeper kine- matics assessment. We employed raw and quaternions data
and we evaluated a series of machine learning algorithms that
discriminate dive types (i.e. binary classification) and dives from
other types of specific motions (i.e. multi-class classification)
directly from the data. Our results demonstrate that XGBoost outperforms
other approaches in terms of performance when considering both raw and
quaternions, essentially benefiting from both types of data.
Additionally, each prediction of the model is accompanied by an
explanation of how each sensed motion component contributes to
describing a specific goalkeeper’s action captured by the model. The
explainable predictions along with the efficient deployment of XGboost
were decisive in our applied study. We evaluated our methodology on a
first batch of experiments using online available data from 7
goalkeepers during 30 minutes-long training sessions.