Probabilistic movement models and zones of control
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In: Machine Learning, Vol. 108, No. 1, 15.01.2019, p. 127-147.
Research output: Journal contributions › Journal articles › Research › peer-review
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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 -