kloppy 0.3.0

Standardizing soccer tracking- and event data

kloppy: standardizing soccer tracking- and event data

klop·pen (klopte, heeft geklopt) - juist zijn; overeenkomen, uitkomen met: dat klopt, dat kan kloppen is juist; dat klopt als een zwerende vinger dat is helemaal juist

What is it?

kloppy is a Python package providing (de)serializers for soccer tracking- and event data, standardized data models, filters, and transformers designed to make working with different tracking- and event data like a breeze. It aims to be the fundamental building blocks for loading, filtering and tranforming tracking- and event data.

Main Features

Here are just a few of the things that kloppy does well:

• Directly load Public datasets to get started right away.
• Understandable Standardized models for tracking- and event datasets
• Out-of-the-box (De)serializing tracking- and event data from different source into standardized models and visa-versa
• Flexible pitch dimensions transformer for changing a dataset pitch dimensions from one to another (eg OPTA's 100x100 -> TRACAB meters)
• Intelligent orientation transforming orientation of a dataset (eg from TRACAB fixed orientation to "Home Team" orientation)

Where to get it

The source code is currently hosted on GitHub at: https://github.com/PySport/kloppy

Installers for the latest released version are available at the Python package index.

# or PyPI
pip install kloppy

Quickstart

We added some helper to get started really quickly. The helpers allow eay loading, transforming and converting to pandas of tracking data.

from kloppy import (
    load_metrica_tracking_data, 
    load_tracab_tracking_data,
    load_epts_tracking_data, 
    to_pandas, 
    transform
)

# metrica data
data_set = load_metrica_tracking_data('home_file.csv', 'away_file.csv')
# or tracab
data_set = load_tracab_tracking_data('meta.xml', 'raw_data.txt')
# or epts
data_set = load_epts_tracking_data('meta.xml', 'raw_data.txt')

data_set = transform(data_set, pitch_dimensions=[[0, 108], [-34, 34]])
pandas_data_frame = to_pandas(data_set)

Public datasets / Very quick start

More and more companies are publishing (demo) datasets to get you started. Inspired by the tensorflow_datasets package, we added a "dataset loader" which does all the heavy lifting for you: find urls, download files, organize and load them.

from kloppy import datasets

data_set = datasets.load("metrica_tracking", options={'sample_rate': 1./12, 'limit': 10})

Standardized models

Most providers use different names for the same thing. This module tries to model the real world as much as possible. Understandable models are important and in some cases this means performance is subordinate to models that are easy to reason about. Please browse to source of domain.models to find our the models that are available.

(De)serializing data

When working with tracking- or event data we need to deserialize it from the format the provider uses. kloppy will provide both deserializing as serializing. This will make it possible to read format one, transform and filter and store in a different format.

from kloppy import TRACABSerializer

serializer = TRACABSerializer()

with open("tracab_data.dat", "rb") as raw, \
        open("tracab_metadata.xml", "rb") as meta:

    data_set = serializer.deserialize(
        inputs={
            'raw_data': raw,
            'meta_data': meta
        },
        options={
            "sample_rate": 1 / 12
        }
    )
    
    # start working with data_set

or Metrica data

from kloppy import MetricaTrackingSerializer

serializer = MetricaTrackingSerializer()

with open("Sample_Game_1_RawTrackingData_Away_Team.csv", "rb") as raw_away, \
        open("Sample_Game_1_RawTrackingData_Home_Team.csv", "rb") as raw_home:

    data_set = serializer.deserialize(
        inputs={
            'raw_data_home': raw_home,
            'raw_data_away': raw_away
        },
        options={
            "sample_rate": 1 / 12
        }
    )
    
    # start working with data_set

or EPTS data

from kloppy import EPTSSerializer

serializer = EPTSSerializer()

with open("raw_data.txt", "rb") as raw, \
        open("metadata.xml", "rb") as meta:

    data_set = serializer.deserialize(
        inputs={
            'raw_data': raw,
            'meta_data': meta
        },
        options={
            "sample_rate": 1 / 12
        }
    )
    
    # start working with data_set

Transform the pitch dimensions

Data providers use their own pitch dimensions. Some use actual meters while others use 100x100. Use the Transformer to get from one pitch dimensions to another one.

from kloppy.domain import Transformer, PitchDimensions, Dimension

# use deserialized `data_set`
new_data_set = Transformer.transform_data_set(
    data_set,
    to_pitch_dimensions=PitchDimensions(
        x_dim=Dimension(0, 100),
        y_dim=Dimension(0, 100)
    )
)

Transform the orientation

Data providers can use different orientations. Some use a fixed orientation and others use ball owning team.

from kloppy.domain import Transformer, Orientation

new_data_set = Transformer.transform_data_set(
    data_set,
    to_orientation=Orientation.BALL_OWNING_TEAM
)

Transforming pitch dimensions and orientation at the same time

from kloppy.domain import Transformer, PitchDimensions, Dimension, Orientation

# use deserialized `data_set`
new_data_set = Transformer.transform_data_set(
    data_set,
    to_pitch_dimensions=PitchDimensions(
        x_dim=Dimension(0, 100),
        y_dim=Dimension(0, 100)
    ),
    to_orientation=Orientation.BALL_OWNING_TEAM
)

TODO List

Data models

• Automated tests
• Pitch
• Tracking
• Event

Tracking data (de)serializers

• Automated tests
• TRACAB
• MetricaSports
• FIFA EPTS
• BallJames

Event data (de)serializers

• Automated tests
• OPTA
• StatsBomb
• MetricaSports

Transformers

• Automated tests
• Transform pitch dimensions
• Transform orientation of points

Filters

• Automated tests
• Smoothing filters for tracking dataset

Helpers

• Load tracking data
• Transform pitch dimensions and orientation
• Export to pandas dataframe

Importers

• Automated tests
• Pandas dataframe

Exporters

• Automated tests
• Pandas dataframe
• SPADL json