Pregled bibliografske jedinice broj: 1237772
Machine learning for data analysis in football: A survey of methods and problems
Machine learning for data analysis in football: A survey of methods and problems // Proceedings of the 33rd International DAAAM Virtual Symposium "Intelligent Manufacturing & Automation" / Katalinić, Branko (ur.).
Beč, Austrija; online: DAAAM International Vienna, 2022. str. 0503-0510 doi:10.2507/33rd.daaam.proceedings.070 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1237772 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Machine learning for data analysis in football: A
survey of methods and problems
Autori
Tokić, Saša ; Panjkota, Ante ; Matetić, Maja
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 33rd International DAAAM Virtual Symposium "Intelligent Manufacturing & Automation"
/ Katalinić, Branko - : DAAAM International Vienna, 2022, 0503-0510
ISBN
978-3-902734-36-5
Skup
33rd DAAAM International Symposium
Mjesto i datum
Beč, Austrija; online, 27.10.2022. - 28.10.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
spatiotemporal ; sports analytics, event data ; deep neural networks, machine learning
Sažetak
Machine learning is growing exponentially, and its applications are getting more and more traction in the sports analysis community in recent years. The application of machine learning methods on spatiotemporal data in sports like football is getting attention from football clubs, academics, and amateur analysts and is the main focus of this survey. This survey analyses and identifies current trends in research papers and literature to determine current and future applications in football analytics using spatiotemporal data.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Projekti:
--uniri-drustv-18-122 - Dubinska analiza tokova podataka za pametno upravljanje hladnim lancem (SmaCC) (SMACC) (Matetić, Maja) ( CroRIS)
Ustanove:
Sveučilište u Zadru,
Fakultet informatike i digitalnih tehnologija, Rijeka
