Deep Learning from Spatial Relations for Soccer Pass Prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning from Spatial Relations for Soccer Pass Prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep Learning from Spatial Relations for Soccer Pass Prediction

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-26999S" target="_blank" >GA17-26999S: Hluboké relační učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Machine Learning and Data Mining for Sports Analytics

ISBN

978-3-030-17273-2

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

8

Strana od-do

159-166

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Dublin

Datum konání akce

10. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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