impectPy

A package provided by: Impect GmbH

Version: v2.4.5

Updated: June 26th 2025

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Supported API Version: V5
For older versions, please see list below:

• API V4: https://github.com/ImpectAPI/impectPy/tree/v1.0.3
• API V3: not supported by this package

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Introduction

The goal of the impectPy package is to provide an easy way for Impect Customers to access data from the customer API. This API includes basic information about competitions, competition iterations, and matches as well as event data and aggregated scorings per player and position on match and season level.

Installation

You can install the latest version of impectPy from GitHub with:

pip install git+https://github.com/ImpectAPI/impectPy.git@v2.4.5

Usage

Getting started

Before accessing any data via our API, you will need to request a bearer token for authorization. You can get this authorization token using the following code snippet:

import impectPy as ip
import pandas as pd

# define login credentials
username = "yourUsername"
password = "yourPassword"

# get access token
token = ip.getAccessToken(username=username, password=password)

This access token is a requirement to use any of the functions that requests data from the API. We recommend to first get a list of competition iterations that are enabled for your account.

Retrieve Basic Information

# get list of iterations
iterations = ip.getIterations(token=token)

# print iterations to console
iterations

If any iteration you were expected to see is not listed, please contact your sales representative. Now let’s assume you are interested in data for 2022/23 season of the 1. Bundesliga (iteration = 518). The following snippet gets you a list of matches for this iteration:

# get matches for iteration
matchplan = ip.getMatches(iteration=518, token=token)

# print matches to console
matchplan

The column available denotes whether a given match has been tagged by Impect and the data is available to you.

Retrieve Match Level Data

Let's assume you are interested in the FC Bayern München vs Borussia Dortmund game from April 1st 2023 (matchId = 84344) and want to retrieve event level data as well as team formation, starting position and substitution data. As the functions allows for multiple games to be requested at once, we need to wrap the matchId into a list. Hence, to request data for this game, run the following code snippet:

# define matches to get event data for
matches = [84344]

# get event data for matches
events = ip.getEvents(
    matches=matches, 
    token=token,
    include_kpis=True,
    include_set_pieces=True
)

# get match info
formations = ip.getFormations(matches, token)
substitutions = ip.getSubstitutions(matches, token)
starting_positions = ip.getStartingPositions(matches, token)

# print first few rows from events dataframe to console
events.head()

You can access the aggregated scores per player and position or per squad for this match in a similar way. You can also find more detailed data around set piece situations within our API. Also, we provide you with IMPECT scores and ratios that you might know from our Scouting and Analysis portals. On player level, these are calculated across positions which is why you have to supply the function with a list of positions your want to retrieve data for:

# define matches to get further data for
matches = [84344]

# get set piece data including KPI aggregates
setPieces = ip.getSetPieces(matches=matches, token=token)

# get kpi matchsums for match per player and position
playerMatchsums = ip.getPlayerMatchsums(matches=matches, token=token)

# get kpi matchsums for match per squad
squadMatchsums = ip.getSquadMatchsums(matches=matches, token=token)

# define positions to get scores aggregated by
positions = ["LEFT_WINGBACK_DEFENDER", "RIGHT_WINGBACK_DEFENDER"]

# get player scores and ratios for match and positions per player
playerMatchScores = ip.getPlayerMatchScores(
    matches=matches,
    positions=positions,
    token=token
)

# get squad scores and ratios for match per squad
squadMatchScores = ip.getSquadMatchScores(matches=matches, token=token)

In case you wish to retrieve data for multiple matches, we suggest using the following method to do so in order to minimize the amount of requests sent to the API. Let’s also get the event data for the RB Leipzig vs FSV Mainz 05 game (matchId = 84350) from the same day:

# define list of matches
matches = [84344, 84350]

# apply getEvents function to a set of matchIds
events = ip.getEvents(
    matches=matches, 
    token=token,
    include_kpis=True,
    include_set_pieces=True
)

# get set piece data including KPI aggregates
setPieces = ip.getSetPieces(matches=matches, token=token)

# get matchsums for matches per player and position
playerMatchsums = ip.getPlayerMatchsums(matches=matches, token=token)

# get matchsums for matches per squad
squadMatchsums = ip.getSquadMatchsums(matches=matches, token=token)

# define positions to get scores aggregated by
positions = ["LEFT_WINGBACK_DEFENDER", "RIGHT_WINGBACK_DEFENDER"]

# get player scores and ratios for match and positions per player
playerMatchScores = ip.getPlayerMatchScores(
    matches=matches,
    positions=positions,
    token=token
)

# get squad scores and ratios for match per squad
squadMatchScores = ip.getSquadMatchScores(matches=matches, token=token)

Retrieve Iteration Level Data

Starting from API version V5, we also offer an endpoint to get KPI average values per iteration on player as well as squad level. These averages are calculated by dividing the kpi sum of all individual matches by the sum of matchShares the player accumulated at a given position. On a team level we divide the score by the amount of matches played by the team. Also, we provide you with IMPECT scores and ratios that you might know from our Scouting and Analysis portals. On player level, these are calculated across positions which is why you have to supply the function with a list of positions your want to retrieve data for. Let's assume you were interested in wing backs in the 2022/2023 Bundesliga season, then you could use this code snippet:

# define iteration ID
iteration = 518

# define positions to get scores aggregated by
positions = ["LEFT_WINGBACK_DEFENDER", "RIGHT_WINGBACK_DEFENDER"]

# get player kpi averages for iteration
playerIterationAverages = ip.getPlayerIterationAverages(
    iteration=iteration,
    token=token
)

# get squad kpi averages for iteration
squadIterationAverages = ip.getSquadIterationAverages(
    iteration=iteration,
    token=token
)

# get player scores and ratios for iteration and positions
playerIterationScores = ip.getPlayerIterationScores(
    iteration=iteration,
    positions=positions,
    token=token
)

# get squad scores and ratios for iteration
squadIterationScores = ip.getSquadIterationScores(
    iteration=iteration,
    token=token
)

# get squad rating for iteration
squadRatings = ip.getSquadRatings(iteration=iteration, token=token

You can now also retrieve the positional profile scores for players via our API. This includes profiles that you created through the scouting portal. The function requires a positional input that determines which matchShares to consider when computing the scores. In the below example, all matchShares that a player played as either a left back or a right back are included for profile score calculation.

# define iteration ID
iteration = 518

# define positions to get scores aggregated by
positions = ["LEFT_WINGBACK_DEFENDER", "RIGHT_WINGBACK_DEFENDER"]

# get player profile scores
playerProfileScores = getPlayerProfileScores(iteration, positions, token)

Please keep in mind that Impect enforces a rate limit of 10 requests per second per user. A token bucket logic has been implemented to restrict the amount of API calls made on the client side already. The rate limit is read from the first limit policy sent back by the API, so if this limit increases over time, this package will act accordingly.

SportsCodeXML

It is also possible to convert a dataframe containing event data into an XML file, that can be imported into Videotools such as FOCUS. The XML can be customized to a certain degree using the following in put variables:

• codeTag: Customize code tag selection (Choose what goes into the code tag)
• lables: Customize labels included (provide a list of labels to be included)
• kpis: Customize KPIs included (provide a list of KPIs to be included)
• labelSorting: Enable/Disable label sorting (Labels and KPIs are usually prefixed with a sorting number (e.g. 01 | ) or the word KPI: to enable easier filtering in your video tool.)
• sequencing: Disable sequencing (A sequence of RECEPTION > DRIBBLE > PASS is split into 3 instances: RECEPTION, DRIBBLE, PASS)
• buckets: Disable Label/KPI buckets (e.g. conversion from value 0.1 to bucket [0,1[)

To see a full list of available codeTags, labels, KPIs and allowed combinations of these, please see the beginning of the function definition.

Please make sure to only retrieve event data for one game at a time. Let's use the Bayern vs Dortmund game from earlier as an example:

# define matchId
matches = [84344]

# get event data for matchId
events = ip.getEvents(matches=matches, token=token)

# define lead and lag time in seconds
lead = 3
lag = 3

# define period start offsets from video start in seconds
p1Start = 16 # first half kickoff happens after 16 seconds in your video file
p2Start = 48 * 60 + 53 # first half kickoff happens after 48 minutes and 53 seconds in your video file
p3Start = 0 # set to timestamp of the kickoff of the first half of extra time
p4Start = 0 # set to timestamp of the kickoff of the second half of extra time
p5Start = 0 # set to timestamp of the first penalty of the penalty shootout

# generate xml
xml_tree = ip.generateXML(
    events=events,
    lead=lead,
    lag=lag,
    p1Start=p1Start,
    p2Start=p2Start,
    p3Start=p3Start,
    p4Start=p4Start,
    p5Start=p5Start,
    codeTag="playerName",  # Use the playerName for the Code Tag
    labels=["action", "opponents"],  # defaults to None to inlcude all available labels
    kpis=["BYPASSED_OPPONENTS", "BYPASSED_DEFENDERS"],  # defaults to None to inlcude all available KPIs
    labelSorting=False,  # Disable sorting prefixes
    sequencing=False,  # Disable merging of consecutive events by the same player into one sequence
    buckets=False  # Use precise KPI and label values instead of predefined buckets
)

# write to xml file
with open(f"match{matches[0]}_"
          # add home team name
          f"{events.homeSquadName.unique().tolist()[0].replace(' ', '_')}"
          f"_vs_"
          # add away team name
          f"{events.awaySquadName.unique().tolist()[0].replace(' ', '_')}"
          f".xml",
          "wb") as file:
    xml_tree.write(file,
                   xml_declaration=True,
                   encoding='utf-8',
                   method="xml")

Object-Oriented Package Version

Since version 2.4.0, there is another way to call the familiar functions in a more object-oriented way. An object of the class "Impect" can be used to query the API. This new object offers a slightly enhanced performance and stores your token as an object attribute. This means you no longer have to include it in every function call. This new IMPECT object can be used as shown in the example below:

from impectPy import Impect

# define login credentials
username = "yourUsername"
password = "yourPassword"

# create Impect instance and login
api = Impect()
api.login(username, password)

# define iteration ID
iteration = 518

# define matchId
matches = [84344]

# define positions to get scores/profiles aggregated by
positions = ["LEFT_WINGBACK_DEFENDER", "RIGHT_WINGBACK_DEFENDER"]

# get iterations
iterations = api.getIterations()

# get squad ratings
ratings = api.getSquadRatings(iteration)

# get matches
matchplan = api.getMatches(iteration)

# get match info
formations = api.getFormations(matches)
substitutions = api.getSubstitutions(matches)
startingPositions = api.getStartingPositions(matches)

# get match events
events = api.getEvents(matches, include_kpis=True, include_set_pieces=True)

# get set pieces
setPieces = api.getSetPieces(matches)

# get player iteration averages
playerIterationAverages = api.getPlayerIterationAverages(iteration)

# get player matchsums
playerMatchsums = api.getPlayerMatchsums(matches)

# get squad iteration averages
squadIterationAverages = api.getSquadIterationAverages(iteration)

# get squad matchsums
squadMatchsums = api.getSquadMatchsums(matches)

# get player match scores
playerMatchScores = api.getPlayerMatchScores(matches, positions)

# get squad match scores
squadMatchScores = api.getSquadMatchScores(matches)

# get player iteration scores
playerIterationScores = api.getPlayerIterationScores(iteration, positions)

# get squad iteration scores
squadIterationScores = api.getSquadIterationScores(iteration)

# get player profile scores
playerProfileScores = api.getPlayerProfileScores(iteration, positions)

Final Notes

Further documentation on the data and explanations of variables can be found in our glossary.