# PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories

arXiv: https://arxiv.org/abs/2602.12080 · HTML: https://mlsanity.com/paper/2602.12080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 82 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
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- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://mlsanity.com/paper/2112.08726/) (2021)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [Leapfrog Diffusion Model for Stochastic Trajectory Prediction](https://mlsanity.com/paper/2303.10895/) (2023)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Fast Structured Decoding for Sequence Models](https://mlsanity.com/paper/1910.11555/) (2019)
- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://mlsanity.com/paper/1803.07612/) (2018)
- [DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting](https://mlsanity.com/paper/2005.12661/) (2020)
- [What Players do with the Ball: A Physically Constrained Interaction Modeling](https://mlsanity.com/paper/1511.06181/) (2015)
- [Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling](https://mlsanity.com/paper/2503.18589/) (2025)
- [baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling](https://mlsanity.com/paper/2102.03291/) (2021)
- [Interaction Modeling with Multiplex Attention](https://mlsanity.com/paper/2208.10660/) (2022)
- [A Survey on Recent Advances in Sequence Labeling from Deep Learning Models](https://mlsanity.com/paper/2011.06727/) (2020)
- [baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents](https://mlsanity.com/paper/2104.11980/) (2021)
- [Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM](https://mlsanity.com/paper/2306.08206/) (2023)
- [TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports](https://mlsanity.com/paper/2410.17785/) (2024)
- [Automatic event detection in football using tracking data](https://mlsanity.com/paper/2202.00804/) (2022)
- [Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations](https://mlsanity.com/paper/2302.06569/) (2023)
- [Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers](https://mlsanity.com/paper/2010.04362/) (2020)
- [Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories](https://mlsanity.com/paper/1710.02255/) (2017)
- [SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data](https://mlsanity.com/paper/2206.10926/) (2022)
- [A Graph Attention Based Approach for Trajectory Prediction in Multi-agent Sports Games](https://mlsanity.com/paper/2012.10531/) (2020)
- [Masked Conditional Random Fields for Sequence Labeling](https://mlsanity.com/paper/2103.10682/) (2021)
- [Constraining Linear-chain CRFs to Regular Languages](https://mlsanity.com/paper/2106.07306/) (2021)
- [Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)](https://mlsanity.com/paper/2505.15820/) (2025)

## Abstract (arXiv)

Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation. Although prior work has explored automatic event detection using player and ball trajectories, ball tracking also remains difficult to scale due to high infrastructural and operational costs. As a result, comprehensive data collection in soccer is largely confined to top-tier competitions, limiting the broader adoption of data-driven analysis in this domain. To address this challenge, this paper proposes PathCRF, a framework for detecting on-ball soccer events using only player tracking data. We model player trajectories as a fully connected dynamic graph and formulate event detection as the problem of selecting exactly one edge corresponding to the current possession state at each time step. To ensure logical consistency of the resulting edge sequence, we employ a Conditional Random Field (CRF) that forbids impossible transitions between consecutive edges, where emission and transition scores are dynamically computed from edge embeddings produced by a socio-temporal backbone architecture. During inference, the most probable edge sequence is obtained via Viterbi decoding, and events such as ball controls or passes are detected whenever the selected edge changes between adjacent time steps. Experiments show that PathCRF produces accurate, logically consistent possession paths, enabling reliable downstream analyses while substantially reducing the need for manual event annotation. The source code is available at https://github.com/hyunsungkim-ds/pathcrf.git.