CatapultPy

A lightweight Python wrapper for the Catapult Sports OpenField API. This package is modeled after CatapultR and is designed to make it easier to pull data into Python for analysis.

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Table of Contents

• Installation
• Basic Functions
• Activity-Based Functions
• Aggregated Data
• 10Hz Data

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Installation

Install via pip:

pip install CatapultPy

Basic Functions

token = ofCreateToken(api_key, region = "us")

Creates the token to call the API. Must include your region name being "us", "eu", "au", "cn".

ofGetActivities(token)

Returns a pandas dataframe of all of the activities.

ofGetAthletes(token)

Get a Pandas Dataframe of all the athletes within a dataframe.

ofGetParams(token)

Return a Pandas Dataframe of all paramaters.

Activity based Functions

ofGetActivitiesAthletes(token, activity_id)

Returns a Pandas dataframe of all the Athletes from within a activity. Must input the activity id.

ofGetActivitesPeriod(token, activity_id)

Return a Pandas dataframe of all the periods within an activity.

ofGetActivitiesTags(token, activity_id)

Return a Pandas dataframe of all the tags within a activity.

ofGetActivitiesDevices(token, acitvity_id)

Return a Pandas Dataframe of all the devices within a activity.

ofGetActivityEvents(token, activity_id, athlete_id, events = ["ima_jumps", "baseball_swing"])

Return a Pandas Dataframe of all the events for a single athlete in a activity. events must be listed in a list format.

ofGetActivityEfforts(token, activity_id, athlete_id, efforts= ['acceleration', 'velocity'])

Return a Pandas Dataframe of all the efforts for a single athlete in a activity. Effort can be acceleartion, velocity or both. It is defaulted as both.

Aggragated data

ofGetStats(token,
            params = ["athlete_name", "date", "start_time", "end_time", "position_name", 
                    "total_distance", "total_duration", "total_player_load", "max_vel", 
                    "hsr_efforts", "max_heart_rate", "mean_heart_rate", 
                    "period_id", "period_name", "activity_name"],
            group_by = ["athlete", "period", "activity"],
            filters = {
                "name" : "activity_id",
                "comparison" : "=",
                "values" : ["activity_id_1", "activity_id_2"]
            })

Returns a Pandas Dataframe of aggragated stats. params and group_by must be inputed as lists. Filters must be imputed as a dicitonary, values must be implemented as a list of activity_id's.

10Hz Data

ofGetActivity10hz(token, activity_id, athlete_id)

Returns a Pandas Dataframe of 10 Hz data of an athlete from within an activity.