Metadata-Version: 2.1
Name: codeball
Version: 0.0.3
Summary: Tactical analysis of football/soccer games
Home-page: https://github.com/metrica-sports/codeball
Author: Bruno Dagnino
Author-email: bruno@metrica-sporst.com
License: UNKNOWN
Description: # codeball: tactical analysis of football / soccer games
        
        [![PyPI Latest Release](https://img.shields.io/pypi/v/codeball.svg)](https://pypi.org/project/codeball/)
        ![](https://img.shields.io/github/license/metrica-sports/codeball)
        ![](https://img.shields.io/pypi/pyversions/codeball)
        [![Powered by Metrica Sports](https://img.shields.io/badge/Powered%20by-Metrica%20Sports-green)](https://metrica-sports.com/)
        --------
        
        ## Why codeball
        
        While there are several pieces of code / repositories around that provide different tools and bits of codes to do tactical analysis of individual games, there is no centralized place in which they live. Codeball is being developed with the goal of being the central repository for tactical analysis of individual games.
        
        ## What can you do with it
        
        There are 3 main things you can do with codeball
        
        #### Work with tracking and event data
        
        - Codeball creates subclasses of *Pandas DataFrames* for events and tracking data; and provides you with handy methods to work with the data.
        - Work with or create your own tactical models like *Zones* so that you can for example do `game_dataset.events.into(Zone.OPPONENT_BOX)` and it will return a DataFrame only with the events into the opponents box. You can also chain methods, like `game_dataset.events.type("PASS").into(Zone.OPPONENT_BOX)` and will return only passes into the box.
        - Easily access tactical tools or methods like computing passes networks, pitch control,EPV models, etc (Not yet implemented, WIP)
        
        #### Create Patterns to analyze the game
        
        - Analyze games based on Patterns. A Pattern is a unit of analysis that looks for moments in the game in which a certain thing happens. That certain thing is defined inside the Pattern, but codeball provides tools to easily create them, configure them and export them in different formats for different platforms.
        - You can create your own patterns, or also use the ones provided with the package and configure them to your liking.
        
        #### Add annotations to the events for Metrica Play
        
        - Codeball incorporates all the annotations models and API information needed to import events with annotations into Metrica Play. - You can add directly from the code any visualization available in Metrica Play  (spotlights, rings, future trail, areas, drawings, text, etc) to any event.
        
        ## Supported Data Providers
        
        This package is very much WIP. At the moment it only works based on Metrica Sports Elite datasets. However, it uses Kloppy to read in the data so that in the near future will support data from any provider.
        
        ## Trying it out
        
        There are no open source Elite datasets at the moment that work with this package. However if you are interested in testing it out and developing your own patterns and/or test them in Metrica Play reach out to bruno@metrica-sports.com or @brunodagnino on Twitter.
        
        ## Install it / contribute
        
        While created and maintained by Metrica Sports, it's distributed under an MIT license and it welcomes contributions from members of the community, clubs and other companies.
        
        The source code is currently hosted on GitHub at: https://github.com/metrica-sports/codeball
        
        Installers for the latest released version are available at the [Python package index](https://pypi.org/project/codeball).
        
        ```sh
        pip install codeball
        ```
        
        ## Documentation
        
        There is no oficial documentation yet, but it's coming :)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: development
