Skip to main content

openstarlab event modeling package

Project description

OpenSTARLab Event Modeling package

Documentation Status dm ArXiv Discord

Introduction

The OpenSTARLab Event package is the fundamental package for event modeling. It is designed to provide a simple and efficient way to train, inference, and simulate events. This package supports the data preprocessed by the OpenSTARLab PreProcessing package.

This package is continuously evolving to support future OpenSTARLab projects. If you have any suggestions or encounter any bugs, please feel free to open an issue.

Soccer Event Modeling

Table: Comparison of model performance on soccer event prediction

Note: Arrows indicate whether a higher (↑) or lower (↓) value is better.
Models are ranked by publication year. Bold values indicate the best performance (unrounded). For more details refer to our paper ArXiv

Wyscout Dataset

Model (Year) Action Acc. ↑ Action F1 ↑ Time-MAE ↓ X-MAE ↓ Y-MAE ↓ FLOPs Num Params
MAJ 0.57 0.08 3.60 18.97 52.55 - -
Seq2Event (2022) 0.67 0.16 3.41 7.11 15.72 112M 135K
NMSTPP (2023) 0.67 0.17 3.34 6.94 15.08 296M 121K
LEM_1 (2024) 0.67 0.17 3.07 8.34 21.44 50M 98K
LEM_3 (2024) 0.67 0.20 2.69 7.62 21.83 20M 39K
FMS (2024) 0.67 0.16 3.27 11.27 24.19 930M 782K

StatsBomb Dataset

Model (Year) Action Acc. ↑ Action F1 ↑ Time-MAE ↓ X-MAE ↓ Y-MAE ↓ FLOPs Num Params
MAJ 0.40 0.06 2.76 20.72 33.32 - -
Seq2Event (2022) 0.65 0.23 2.43 7.22 6.86 4.03B 413K
NMSTPP (2023) 0.65 0.23 2.53 7.38 6.86 2.02B 217K
LEM_1 (2024) 0.65 0.24 2.23 7.36 8.21 66M 128K
LEM_3 (2024) 0.66 0.25 2.07 7.07 8.32 19M 38K
FMS (2024) 0.65 0.24 2.35 7.77 8.82 3.66B 1.29M

Installation

  • Install pytorch (recommended version 2.4.0 linux pip python3.8 cuda12.1)
pip install torch torchvision torchaudio
  • To install this package via PyPI
pip install openstarlab-event
  • To install manually
git clone git@github.com:open-starlab/Event.git
cd ./Event
pip install -e .

Current Features

Sports

RoadMap

  • Release the package
  • Provide pre-trained models

Other Information

Development torch version

version 2.4.0 linux pip python3.8 cuda12.1 

Developer

Calvin Yeung
Calvin Yeung

💻
Keisuke Fujii
Keisuke Fujii

🧑‍💻

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

openstarlab_event-0.1.25.tar.gz (63.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

openstarlab_event-0.1.25-py3-none-any.whl (92.6 kB view details)

Uploaded Python 3

File details

Details for the file openstarlab_event-0.1.25.tar.gz.

File metadata

  • Download URL: openstarlab_event-0.1.25.tar.gz
  • Upload date:
  • Size: 63.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.8.19

File hashes

Hashes for openstarlab_event-0.1.25.tar.gz
Algorithm Hash digest
SHA256 1d7ebf59e42af0fe70b90d4d18a50e646a7dbcdf531e28062d7bfd364fee45d8
MD5 be77a02b2209c8c81c788aa1f383021d
BLAKE2b-256 6212b969a1da7ce5641d62c9e2864530cc38d2b5cbaedf5554cb6ea9d58c94cb

See more details on using hashes here.

File details

Details for the file openstarlab_event-0.1.25-py3-none-any.whl.

File metadata

File hashes

Hashes for openstarlab_event-0.1.25-py3-none-any.whl
Algorithm Hash digest
SHA256 dbdaa714c7b5e9186c09a704512de0ef4df96d622d817172aec3f6260de13174
MD5 ca50d8dfb8ea86cdc7025781e9cede56
BLAKE2b-256 331982b077288f8dcb1cc045452e0cb2e100e04ca823fa4222172755b19a39e2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page