Technological innovations of recent years, particularly in the field of tracking systems, are leading to an increasing volume of data in sports. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The minimal underlying data set necessary for replication of this study is available within the paper and its Supporting Information file.įunding: This work was supported by the German Research Foundation (DFG) and the Technische Universität München within the funding programme Open Access Publishing (websites: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. Received: NovemAccepted: JPublished: July 10, 2017Ĭopyright: © 2017 Link, Hoernig.
PLoS ONE 12(7):Įditor: Øyvind Sandbakk, Norwegian University of Science and Technology, NORWAY The results could improve performance analysis in soccer, help to detect match events automatically, and allow discernment of higher value tactical structures, which is based on individual ball possession.Ĭitation: Link D, Hoernig M (2017) Individual ball possession in soccer. The analysis of ball possession at the player level indicates shortest accumulated IBC times for the central forwards (0:49 ± 0:43 min) and the longest for goalkeepers (1:38 ± 0:58 min), central defenders (1:38 ± 1:09 min) and central midfielders (1:27 ± 1:08 min).
There were 836 ± 424 IBC intervals per match and their number was significantly reduced by -5.1% from the 1 st to 2 nd half. Match analysis showed the following mean values per match: TBP 56:04 ± 5:12 min, TPM 50:01 ± 7:05 min and TBC 17:49 ± 8:13 min. 83 for IBP, and the classification rate for IBC was κ =. The evaluation and application of this approach uses data from 60 matches in the German Bundesliga season of 2013/14, including 69,667 IBA intervals. The degree of ball control exhibited during this phase is classified based on the spatio-temporal configuration of the player controlling the ball, the ball itself and opposing players using a Bayesian network. The machine learning approach used is able to determine how long the ball spends in the sphere of influence of a player based on the distance between the players and the ball together with their direction of motion, speed and the acceleration of the ball. The types of ball possession are classified as Individual Ball Possession (IBC), Individual Ball Action (IBA), Individual Ball Control (IBC), Team Ball Possession (TBP), Team Ball Control (TBC) und Team Playmaking (TPM) according to different starting points and endpoints and the type of ball control involved. This paper describes models for detecting individual and team ball possession in soccer based on position data.