000218395 001__ 218395
000218395 005__ 20190812205913.0
000218395 037__ $$aCONF
000218395 245__ $$aAnalyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques
000218395 269__ $$a2016
000218395 260__ $$c2016
000218395 336__ $$aConference Papers
000218395 520__ $$aThis paper proposes a relational learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial configurations of the players on the court) and temporal , that is, the order of events and positions, aspects of the game. We analyze both the men’s and women’s final match from the 2014 FIVB Volleyball World Championships, and are able to identify several interesting and relevant strategies from the matches.
000218395 6531_ $$aSports analytics
000218395 6531_ $$aSpatial data
000218395 6531_ $$aStrategy detection
000218395 700__ $$aVanHaaren, Jan
000218395 700__ $$0242722$$g193130$$aBen Shitrit, Horesh
000218395 700__ $$aDavis, Jesse
000218395 700__ $$aFua, Pascal$$g112366$$0240252
000218395 7112_ $$dAugust, 2016$$cSan Francisco, CA$$aConference on Knowledge Discovery and Data Mining
000218395 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/218395/files/KDD_submission_725.pdf$$s206928
000218395 909C0 $$xU10659$$pCVLAB$$0252087
000218395 909CO $$ooai:infoscience.tind.io:218395$$qGLOBAL_SET$$pconf$$pIC
000218395 917Z8 $$x112366
000218395 937__ $$aEPFL-CONF-218395
000218395 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000218395 980__ $$aCONF