TY - JOUR

T1 - Probabilistic movement models and zones of control

AU - Brefeld, Ulf

AU - Lasek, Jan

AU - Mair, Sebastian

PY - 2019/1/15

Y1 - 2019/1/15

N2 - Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is prone to errors. As a remedy, we propose to estimate individual movement models from positional data and show how to turn these estimates into accurate and realistic zones of control. Our approach accounts for characteristic traits of players, scales with large amounts of data, and can be efficiently computed in a distributed fashion. We report on empirical results.

AB - Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is prone to errors. As a remedy, we propose to estimate individual movement models from positional data and show how to turn these estimates into accurate and realistic zones of control. Our approach accounts for characteristic traits of players, scales with large amounts of data, and can be efficiently computed in a distributed fashion. We report on empirical results.

KW - Business informatics

KW - Movement models

KW - Positional data

KW - Soccer

KW - Zones of Control

KW - traditional Models

KW - Team sports

UR - http://www.scopus.com/inward/record.url?scp=85049144661&partnerID=8YFLogxK

U2 - 10.1007/s10994-018-5725-1

DO - 10.1007/s10994-018-5725-1

M3 - Journal articles

VL - 108

SP - 127

EP - 147

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 1

ER -