Identifying Team Style in Soccer using Formations Learned from Spatiotemporal Tracking Data

被引：57

作者：

Bialkowski, Alina ^{[1
,2
]}

Lucey, Patrick ^{[1
]}

Carr, Peter ^{[1
]}

Yue, Yisong ^{[1
,3
]}

Sridharan, Sridha ^{[2
]}

Matthews, Iain ^{[1
]}

机构：

[1] Disney Res, Pittsburgh, PA 15213 USA

[2] Queensland Univ Technol, Brisbane, Qld 4001, Australia

[3] CALTECH, Pasadena, CA 91125 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW) | 2014年

关键词：

D O I：

10.1109/ICDMW.2014.167

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.

引用

页码：9 / 14

页数：6