# SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data

arXiv: https://arxiv.org/abs/2206.10926 · HTML: https://mlsanity.com/paper/2206.10926/

## 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 (most cited first, 5 of 5)

- [Heterogeneous Change Point Inference](https://mlsanity.com/paper/1505.04898/) (2015)
- [Asymptotic Distribution-Free Change-Point Detection for Multivariate and non-Euclidean Data](https://mlsanity.com/paper/1707.00167/) (2017)
- [Dirichlet Process Hidden Markov Multiple Change-point Model](https://mlsanity.com/paper/1505.01665/) (2015)
- [Asymptotic distribution-free change-point detection for data with repeated observations](https://mlsanity.com/paper/2006.10305/) (2020)
- [6MapNet: Representing soccer players from tracking data by a triplet network](https://mlsanity.com/paper/2109.04720/) (2021)

## Built on by (most cited first, 7 of 7)

- [ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis](https://mlsanity.com/paper/2306.04948/) (2023)
- [Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset](https://mlsanity.com/paper/2306.15664/) (2023)
- [TacEleven: generative tactic discovery for football open play](https://mlsanity.com/paper/2511.13326/) (2025)
- [Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks](https://mlsanity.com/paper/2512.10355/) (2025)
- [Contextual Sprint Classification in Soccer Based on Deep Learning](https://mlsanity.com/paper/2406.15659/) (2024)
- [EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment](https://mlsanity.com/paper/2506.23843/) (2025)
- [PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories](https://mlsanity.com/paper/2602.12080/) (2026)

## 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.