Space and Control in Soccer.

In many team sports, the ability to control and generate space in dangerous areas on the pitch is crucial for the success of a team. This holds, in particular, for soccer. In this study, we revisit ideas from Fernandez and Bornn (2018) who introduced interesting space-related quantities including pitch control (PC) and pitch value. We identify influence of the player on the pitch with the movements of the player and turn their concepts into data-driven quantities that give rise to a variety of different applications. Furthermore, we devise a novel space generation measure to visualize the strategies of the team and player. We provide empirical evidence for the usefulness of our contribution and showcase our approach in the context of game analyses.

Rating Player Actions in Soccer.

We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.

Sensor Measures of Affective Leaning.

The aim of this study was to predict self-report data for self-regulated learning with sensor data. In a longitudinal study multichannel data were collected: self-report data with questionnaires and embedded experience samples as well as sensor data like electrodermal activity (EDA) and electroencephalography (EEG). 100 students from a private university in Germany performed a learning experiment followed by final measures of intrinsic motivation, self-efficacy and gained knowledge. During the learning experiment psychophysiological data like EEG were combined with embedded experience sampling measuring motivational states like affect and interest every 270 s. Results of machine learning models show that consumer grade wearables for EEG and EDA failed to predict embedded experience sampling. EDA failed to predict outcome measures as well. This gap can be explained by some major technical difficulties, especially by lower quality of the electrodes. Nevertheless, an average activation of all EEG bands at T7 (left-hemispheric, lateral) can predict lower intrinsic motivation as outcome measure. This is in line with the personality system interactions (PSI) theory of Julius Kuhl. With more advanced sensor measures it might be possible to track affective learning in an unobtrusive way and support micro-adaptation in a digital learning environment.

Learning to Rate Player Positioning in Soccer.

We investigate how to learn functions that rate game situations on a soccer pitch according to their potential to lead to successful attacks. We follow a purely data-driven approach using techniques from deep reinforcement learning to valuate multiplayer positionings based on positional data. Empirically, the predicted scores highly correlate with dangerousness of actual situations and show that rating of player positioning without expert knowledge is possible.