Please use this identifier to cite or link to this item:
https://doi.org/10.48548/pubdata-1507
Full metadata record
| Field | Value |
|---|---|
| Resource type | Journal Article |
| Title(s) | Rating Player Actions in Soccer |
| DOI | 10.48548/pubdata-1507 |
| Handle | 20.500.14123/1581 |
| Creator | Dick, Uwe  1128098636Tavakol, Maryam Brefeld, Ulf  0000-0001-9600-6463 |
| Abstract | 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. |
| Language | English |
| Keywords | Sports Analytics; Soccer; Graph Networks; Trajectory Prediction; Trajectory Data |
| Year of publication in PubData | 2024 |
| Publishing type | Parallel publication |
| Publication version | Published version |
| Date issued | 2021-07-15 |
| Creation context | Research |
| Faculty / department | Fakultät Wirtschaft |
| Notes | This publication was funded by the Open Access Publication Fund of Leuphana University Lüneburg. |
| Date of Availability | 2024-11-19T14:14:38Z |
| Archiving Facility | Medien- und Informationszentrum (Leuphana Universität Lüneburg  02w2y2t16) |
| Published by | Medien- und Informationszentrum, Leuphana Universität Lüneburg |
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| File | Description | Size | Format | |
|---|---|---|---|---|
Dick_Rating_Player_Actions_in_Soccer.pdf License: 
open-access | 2.47 MB | Adobe PDF | View/Open |
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