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JRM Vol.36 No.3 pp. 603-617
doi: 10.20965/jrm.2024.p0603
(2024)

Paper:

Unveiling Multi-Agent Strategies: A Data-Driven Approach for Extracting and Evaluating Team Tactics from Football Event and Freeze-Frame Data

Calvin Yeung* ORCID Icon, Rory Bunker* ORCID Icon, and Keisuke Fujii*,**,***,†

*Nagoya University
1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan

**RIKEN Center for Advanced Intelligence Project
1-5 Yamadaoka, Suita, Osaka 565-0871, Japan

***PRESTO, Japan Science and Technology Agency
Kawaguchi Center Building, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan

Corresponding author

Received:
October 19, 2023
Accepted:
March 14, 2024
Published:
June 20, 2024
Keywords:
muti-agent, sequential pattern mining, neural point process, deep learning, sports
Abstract

Studying collective behavior in opposing multi-agent teams is crucial across game theory, robotics, and sports analytics. In sports, especially football, team tactics involve intricate strategic spatial and action behaviors displayed as event sequences during possession. Understanding and analyzing these tactics is essential for successful training, strategic planning, and on-field success. While traditional approaches, such as notational and statistical analyses, offer valuable insights into team tactics, they often lack a comprehensive consideration of contextual information, thereby limiting the holistic evaluation of teams’ performances. To bridge this gap and capture the nuanced intricacies of team tactics, we employed advanced methodologies. The sequential pattern mining algorithm PrefixSpan was utilized to extract tactical patterns from possession sequences, enabling a deeper understanding of how teams strategize and adapt during play. Additionally, the neural marked spatio temporal point process (NMSTPP) model was leveraged to model and predict team behaviors, facilitating a fair comparison among teams. The evaluation of team possessions was further enhanced through the innovative holistic possession utilization score metrics, providing a more nuanced assessment of performance. In our experimental exploration, we identified and classified five distinct team tactics, validated the efficacy of the NMSTPP model when integrating StatsBomb 360 data, and conducted a comprehensive analysis of English Premier League teams during the 2022/2023 season. The results were visualized using radar plots and scatter plots with mean shift clustering. Lastly, the potential applications to RoboCup were discussed.

Team tactics performance radar plot

Team tactics performance radar plot

Cite this article as:
C. Yeung, R. Bunker, and K. Fujii, “Unveiling Multi-Agent Strategies: A Data-Driven Approach for Extracting and Evaluating Team Tactics from Football Event and Freeze-Frame Data,” J. Robot. Mechatron., Vol.36 No.3, pp. 603-617, 2024.
Data files:
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