SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data
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Abstract (arXiv)
In fluid team sports such as soccer and basketball, analyzing team formation is one of the most intuitive ways to understand tactics from domain participants' point of view. However, existing approaches either assume that team formation is consistent throughout a match or assign formations frame-by-frame, which disagree with real situations. To tackle this issue, we propose a change-point detection framework named SoccerCPD that distinguishes tactically intended formation and role changes from temporary changes in soccer matches. We first assign roles to players frame-by-frame and perform two-step change-point detections: (1) formation change-point detection based on the sequence of role-adjacency matrices and (2) role change-point detection based on the sequence of role permutations. The evaluation of SoccerCPD using the ground truth annotated by domain experts shows that our method accurately detects the points of tactical changes and estimates the formation and role assignment per segment. Lastly, we introduce practical use-cases that domain participants can easily interpret and utilize.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 5 of its 39 references that are in the map. First posted on arXiv 22 Jun 2022.
Builds on
5 of 39 references.
References · 5
- 2015Heterogeneous Change Point Inferencecited by 4
- 2017Asymptotic Distribution-Free Change-Point Detection for Multivariate and non-Euclidean Datacited by 4
- 2015Dirichlet Process Hidden Markov Multiple Change-point Modelcited by 1
- 2020Asymptotic distribution-free change-point detection for data with repeated observationscited by 1
- 20216MapNet: Representing soccer players from tracking data by a triplet networkcited by 1
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Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 1.
7 papers.
- 2023ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysiscited by 15
- 2023Benchmarking Stroke Forecasting with Stroke-Level Badminton Datasetcited by 8
- 2025TacEleven: generative tactic discovery for football open playcited by 4
- 2025Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networkscited by 2
- 2024Contextual Sprint Classification in Soccer Based on Deep Learningcited by 1
- 2025EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignmentcited by 1
- 2026PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectoriescited by 0
- 2026PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories12 Feb 2026
- 2025Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks11 Dec 2025
- 2025TacEleven: generative tactic discovery for football open play17 Nov 2025
- 2025EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment30 Jun 2025
- 2024Contextual Sprint Classification in Soccer Based on Deep Learning21 Jun 2024
- 2023Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset27 Jun 2023
- 2023ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis8 Jun 2023