openstarlab-phaselearn 0.0.2

openstarlab phaselearn modeling package

OpenSTARLab PhaseLearn Modeling package

Introduction

The OpenSTARLab PhaseLearn package is the fundamental library for modeling and analyzing play phases in sports. It provides a robust framework to train and perform inference on play phases using state-of-the-art spatio-temporal architectures.

Key Concepts

• Definition of Play Phase: The estimation targets are Phases of Play as defined by FIFA.
• This package supports data preprocessed by the OpenSTARLab PreProcessing package.

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Installation

• Install pytorch (recommended version 2.4.0 linux pip python3.9 cuda11.8)

pip install torch torchvision torchaudio

• To install this package via PyPI

pip install openstarlab-phaselearn

• To install manually

git clone [e-mail]:open-starlab/PhaseLearn.git
cd ./PhaseLearn
pip install -e .

Quick Start

Get started with inference using pre-trained models.

1. Download Pre-trained Model: Download the model weights from MODEL URL and place them in the model/ directory.

2. Generate Phase Data: Before estimating, you must convert the raw tracking into the Phase Data format.

   • PreProcessing Documentation

3. Run Inference: Perform phase estimation on your data.

   • Inference Example Guide

Training and Analysis from Scratch

Follow these steps to train your own play phase model using the SoccerTrack-v2 dataset.

1. Data Acquisition:

   • Tracking Data: Obtain the raw data from SoccerTrack-v2.
     • Required Files Guide (Note: Different files are required for Match IDs >= 130000 and < 130000).
   • Phase Annotation Data: Download the play phase annotation data from DATA URL and place them in the data/ directory.

2. Generate Phase Data: Before training, you must convert the raw tracking into the Phase Data format.

   • PreProcessing Documentation

3. Training & Evaluation: Once the data is prepared, you can execute the training pipeline.

   • Train, Inference and Evaluation Example Guide

Repository Layout

PhaseLearn/
├── phase/sports/soccer/         
│   ├── dataloaders/data_module.py
│   ├── inference/inference.py
│   ├── main_class_soccer/main.py
│   ├── models/
│   │   ├── model_yaml/
│   │   │   ├── train_baller2vec.yaml
│   │   │   ├── train_gat_transformer.yaml
│   │   │   ├── train_gcn_transformer.yaml
│   │   │   └── train_transformer.yaml
│   │   ├── baller2vec.py
│   │   ├── gat_transformer.py
│   │   ├── gcn_transformer.py
│   │   └── transformer.py
│   ├── trainers/train.py
│   └── utils/
│       ├── evaluation.py
│       ├── load_train_data.py
│       └── preprocessing.py
├── data/                              # Data storage (gitignored except .gitkeep)
│   ├── phase_annotation_data/
│   │   ├── 117092/
│   │   │   ├── 117092_00_01-04_18_annotation
│   │   │   ├── ...
│   │   ├── ...
│   ├── phase_data/
│   │   ├── bepro/
│   │   │   ├── 117092/117092_main_data
│   │   │   ├── ...
│   │   └── statsbomb_skillcorner/
│   │       ├── 3894537_1018887/3894537_1018887_main_data
│   │       ├── ...
│   └── train_data/bepro/              # generated by preprocessing_data()
│       ├── label_np.bpy
│       └── sequence_np.bpy
└── model/                              # Model storage (gitignored except .gitkeep)
    ├── baller2vec/
    │   ├── ...
    ├── gat_transformer/
    │   ├── 2team_mode/
    │   │   ├── 20251221_155257/run1
    │   │   │   ├── best.pth
    │   │   │   ├── hyperparameters.json
    │   │   │   ├── loss.csv
    │   │   │   └── model_stats.txt
    │   │   ├── ...
    │   ├── ...
    ├── gcn_transformer/
    │   ├── ...
    └── trnsformer/
        ├── ...

RoadMap

• Release the package
• Provide pre-trained models
• Provide phase dataset

Other Information

Development torch version

version 0.0.2 linux pip python3.9 cuda11.8 

Citation

If you use phaselearn modeling in your research, please cite it as follows:

@article{playphase,
  title={Estimating Probability Distributions of FIFA-Defined Phases of Play Based on Inter-Analyst Diversity},
  author={Kento Kuroda, Keisuke Fujii and Yoshinari Kameda},
  journal={International Journal of Computer Science in Sport},
  year={2026},
  volume={XX},
  pages={XXX-XXX},
  publisher={Publisher}
}

Developer

Kento Kuroda 💻

Kenjiro Ide 💻

Keisuke Fujii 🧑‍💻