A method to identify defensive assignments in team-based invasion sports using spatiotemporal trajectories

ABSTRACT

Several works in GIScience propose approaches to identify general motion patterns through the analysis of objects’ trajectories. However, they are not suitable to identify functional relationships in scenarios where domain-dependent motion behaviors exist. In this work, we explore the identification of a particular pattern found in team-based invasion sports. We propose a method to identify defensive assignments between players of opposite teams based on the analysis of their trajectories. A defensive assignment happens when a defensive player blocks or hinders the progress of an opponent player inside his/her field. The defensive assignment can be classified as a behavioral pattern because it combines other behavioral patterns, such as pursuit, evasion, attack and defense. The identification of the assignments takes into account the following aspects of the players’ trajectories: proximity, position, speed and direction. The assessments pointed out that the method provides promising results, achieving 84% of success rate when compared with the analysis of a human specialist.

KEYWORDS:

• Trajectories
• behavioral pattern
• segmentation
• relative motion
• defensive assignment

Data and codes availability statement

The data and codes that support the findings of this study are available in figshare.com with the identifier at the link https://doi.org/10.6084/m9.figshare.12678989. The raw data is available at https://github.com/linouk23/NBA-Player-Movements.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was partially funded by the Foundation of Supporting to Research and Innovation of Santa Catarina State – FAPESC.

Notes on contributors

Yoran E. Leichsenring

Yoran E. Leichsenring earned the BSc in Computer Engineering from the Unisociesc University, Joinville, Brazil (2010), and the MSc in Computer Science by the Graduate Program in Applied Computing from the Santa Catarina State University, Joinville, Brazil (2018). His research interests include the application of soft computing techniques on solving trajectories' semantic segmentation problems. 

Rafael Stubs Parpinelli

Rafael S. Parpinelli received the BSc in Computer Science (2000) from the State University of Maringá (UEM), the MSc in Computer Science (2001), and the PhD in Computer Engineering (2013) from the Federal University of Technology – Paraná (UTFPR). He is presently working as an Associate Professor in the Department of Computer Science and the Graduate Program in Applied Computing of State University of Santa Catarina (UDESC). He is the Head of the Applied Cognitive Computing Group, since its foundation in 2004. His main areas of interest are machine learning, bioinformatics, multiobjective and dynamic optimization, and biologically inspired algorithms.

Fabiano Baldo

Fabiano Baldo received the BSc in Computer Science (2000), the MEng (2003), and the PhD (2008) in Electrical Engineering from Santa Catarina State University (UFSC). He is an associate professor in the Department of Computer Science at Santa Catarina State University (UDESC), Joinville, Brazil. His research interests include the application of meta-heuristics and soft computing techniques in the solution of combinational analysis problems related to moving objects trajectories and vehicle routing problems, and the application of semi-supervised machine learning techniques in the classification of data streams.