Marcotti (formerly named the Football Match Result Database) is a data schema that captures match result data in order to support football research activities. Match result data is defined as historical and publicly-available data that includes the following:

• Friendly matches and matches that make up league, knockout or hybrid (group + knockout) football competitions, and involve either clubs or national team selections.
• Complete data on participating personnel such as players, managers, and match officials.
• Complete top-level data on the football match, including match date, competition name, participating teams, venues, and environmental conditions.
• Complete data on macro-events that occur during a match, including goals, penalties, disciplinary events, substitutions, and penalty shootouts.
• Summary in-match statistics on participating players, including crossing, passing, shooting, defensive, disciplinary, and goalkeeping categories.

The Marcotti data schema is made up of backend-independent SQLAlchemy objects, and club and national team databases are built from these objects.

Marcotti is written in Python and uses the SQLAlchemy package heavily.

While not required, virtualenv is strongly recommended and virtualenvwrapper is very convenient.

Installation instructions:

1. Setup the virtual environment and use pip to install the package:

    $ cd /path/to/working/dir
    $ mkvirtualenv marcotti
    (marcotti) $ pip install git+https://github.com/soccermetrics/marcotti.git[@{release_tag}]
   

2. Run the dbsetup command and answer the setup questions to create configuration and data loading scripts.

   (marcotti-mls) $ dbsetup
   #### Please answer the following questions to setup the folder ####
   Work folder (must exist): [.] /path/to/files
   Logging folder (must exist): [.] /path/to/logs
   Config file name: [local]
   Config class name: [LocalConfig]

   The command will produce three files in the working folder:

   • local.py: A user-defined database configuration file
   • logging.json: Default logging configuration file
   • loader.py: Data loading module

Two data schemas are created - one for clubs, the other for national teams. There is a collection of common data models upon which both schemas are based, and data models specific to either schema.

The common data models are classified into five categories:

• Overview: High-level data about the football competition
• Personnel: Participants and officials in the football match
• Match: High-level data about the match
• Match Events: The main events of the football match
• Statistics: Summary statistics of participating players in the football match
• Suppliers: Mapping data records from outside sources to Marcotti database

The Marcotti wiki contains extensive user documentation of the package.

(c) 2015-2017 Soccermetrics Research, LLC. Created under MIT license. See LICENSE file for details.