Abstract
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.
| Original language | English |
|---|---|
| Journal | Big Data |
| Volume | 7 |
| Issue number | 1 |
| Pages (from-to) | 71-82 |
| Number of pages | 12 |
| ISSN | 2167-6461 |
| DOIs | |
| Publication status | Published - 01.03.2019 |
Bibliographical note
Publisher Copyright:Copyright 2019, Mary Ann Liebert, Inc., publishers
Research areas and keywords
- Informatics
- deep learning
- reinforcement learning
- scoring function
- spatiotemportal data
- Business informatics
ASJC Scopus Subject Areas
- Information Systems and Management
- Computer Science Applications
- Information Systems
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