Deep Learning from Spatial Relations for Soccer Pass Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00326597" target="_blank" >RIV/68407700:21230/19:00326597 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-17274-9_14" target="_blank" >https://doi.org/10.1007/978-3-030-17274-9_14</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-17274-9_14" target="_blank" >10.1007/978-3-030-17274-9_14</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning from Spatial Relations for Soccer Pass Prediction
Original language description
We propose a convolutional architecture for learning representations over spatial relations in the game of soccer, with the goal to predict individual passes between players, as a submission to the prediction challenge organized for the 5th Workshop on Machine Learning and Data Mining for Sports Analytics. The goal of the challenge was to predict the receiver of a pass given location of the sender and all other players. From each soccer situation, we extract spatial relations between the players and a few key locations on the field, which are then hierarchically aggregated within the neural architecture designed to extract possibly complex gameplay patterns stemming from these simple relations. The use of convolutions then allows to efficiently capture the various regularities that are inherent to the game. In the experiments, we show very promising performance of the method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-26999S" target="_blank" >GA17-26999S: Deep relational learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Data Mining for Sports Analytics
ISBN
978-3-030-17273-2
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
159-166
Publisher name
Springer
Place of publication
Cham
Event location
Dublin
Event date
Sep 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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