Unified and Integrated Event Data (UIED)

The UIED is an origninal designed to provide a standardize format for event data in football.

Event Class Standardization

Comparison of event classes across different data sources and formats
Overall	google research football	Statsbomb	Wyscout	DataStadium	BePro
Short Pass	Short Pass	Ground pass	Goal kick	Direct FK - Pass	Pass
		Low pass	Free kick	Indirect FK	Throw In
		Half Start	Simple pass	KickOff	Set Pieces
			Hand pass	HomePass
			Head pass	AwayPass
			Smart pass	PKPass
			Throw in	Through Pass
				ThrowIn
				Feed
				FrickOn
High Pass	High Pass	High pass	High pass
Long Pass	Long Pass	Ground pass	Goal kick	Direct FK - Pass	Free Kick
		Low pass	Free kick	Indirect FK	Goal Kick
			Simple pass	KickOff	Corner Kick
				HomePass
				AwayPass
				PKPass
				Through Pass
				ThrowIn
				Feed
				FrickOn
Shot	Shot	Shot	Shot	Shoot	Shot
			Free kick shot	Direct FK - Shot	Goal
Carry	Sprint	Carry	Acceleration
Dribble	Dribble	Dribble	Touch	Dribble	Dribble To Space
				Touch	Dribble
Cross		Cross	Cross	CK	Cross
		Corner	Free kick cross	Cross

Short Pass and Long Pass are determined by the pass length (45 meters).
“_” : end of possession, “period_over”, and “game_over” is added to end of each possession, period, and game.

Pitch Coordinates Standardization

UIED Format

The UIED format includes the following columns:

match_id (int): Unique identifier for each match.
poss_id (int): Unique identifier for each possession within a match.
team (str): The team associated with the event.
home_team (int): Indicator of whether the team is the home team (1 for home, 0 for away).
action (str): Simplified and normalized description of the event action.
success (int): Indicator of whether the event action was successful (1 for success, 0 for failure).
goal (int): Indicator of whether the event resulted in a goal (1 for goal, 0 for no goal).
home_score (int): The current score of the home team.
away_score (int): The current score of the away team.
goal_diff (int): The goal difference (home_score - away_score).
Period (int): The period of the match (1 for 1st half, 2 for 2nd half, etc.).
Minute (int): The minute within the current period.
Second (float): The second within the current minute.
seconds (float): The total seconds elapsed since the start of the match, adjusted for different periods.
delta_T (float): The time difference between the current event and the previous event in seconds.
start_x (float): The x-coordinate of the event’s starting location, scaled by the field size.
start_y (float): The y-coordinate of the event’s starting location, scaled by the field size.
deltaX (float): The change in the x-coordinate from the previous event.
deltaY (float): The change in the y-coordinate from the previous event.
distance (float): The distance covered by the event.
dist2goal (float): The distance from the event’s starting location to the center of the goal.
angle2goal (float): The angle between the event’s starting location and the goal, in radians.

Examples for Standardizing Multiple Matches

Refer to the data provider pages to convet between single file and multiple file

Example of the UIED format for Wyscout:

import pandas as pd
from preprocessing import Event_data

event_folder = 'path/to/event/folder'
match_folder = 'path/to/match/folder'
max_workers = 1

wyscout_df=Event_data(data_provider='wyscout',event_path=event_folder,match_folder=match_folder,
                        preprocess_method="UIED",max_workers=max_workers).preprocessing()
print(wyscout_df.head())

Example of the UIED format for StatsBomb:

import pandas as pd
from preprocessing import Event_data

event_folder = 'path/to/event/folder'
sb360_folder = 'path/to/sb360/folder'
statsbomb_match_id = '12345'
max_workers = 1

#json/csv file
statsbomb_df=Event_data(data_provider='statsbomb',event_path=event_folder,
                        sb360_path=sb360_folder,preprocess_method="UIED",
                        max_workers=max_workers).preprocessing()
#api
statsbomb_df=Event_data(data_provider='statsbomb',statsbomb_match_id=statsbomb_match_id,
                        preprocess_method="UIED",max_workers=max_workers).preprocessing()

print(statsbomb_df.head())

Example of the UIED format for StatsBomb and SkillCorner:

import pandas as pd
from preprocessing import Event_data

event_folder = 'path/to/event/folder'
tracking_folder = 'path/to/tracking/folder'
match_folder = 'path/to/match/folder'
match_id_df = 'path/to/match_id.csv'
max_workers = 1

df_statsbomb_skillcorner=Event_data(data_provider='statsbomb_skillcorner',
                                        statsbomb_event_dir=event_folder,
                                        skillcorner_tracking_dir=tracking_folder,
                                        skillcorner_match_dir=match_folder,
                                        match_id_df=match_id_df,
                                        preprocess_method="UIED",
                                        max_workers=max_workers).preprocessing()

print(df_statsbomb_skillcorner.head())

Example of the UIED format for DataStadium:

import pandas as pd
from preprocessing import Event_data
data_dir = 'path/to/data/folder' #the dir contain folders that contain the play.csv and tracking.csv files
max_workers = 1

df_datastadium=Event_data(data_provider='datastadium',
                                        event_path=data_dir,
                                        preprocess_method="UIED",
                                        max_workers=max_workers).preprocessing()

print(df_datastadium.head())

Example of the UIED format for SoccerTrackv2:

import pandas as pd
from preprocessing import Event_data
data_dir = path/to/event.csv
tracking_path = path/to/tracking.xml,
meta_data = path/to/meta.xml

df_bepro=Event_data(data_provider='bepro',
                                        event_path=data_dir,
                                        tracking_path=tracking_path,
                                        meta_data=meta_data,
                                        preprocess_method="UIED",
                                        soccertrackv2 = True,
                                        max_workers=max_workers).preprocessing()

print(df_bepro.head())

Example of the UIED format for BePro:

import pandas as pd
from preprocessing import Event_data
data_dir = ["./_1st Half.json", "./_2nd Half.json"] # List of paths to the JSON files containing event data of the same match
tracking_path = path/to/tracking.xml,
meta_data = path/to/meta.xml
match_id = 12345  # Specify the match ID for BePro data or any unique identifier

df_bepro=Event_data(data_provider='bepro',
                                        event_path=data_dir,
                                        tracking_path=tracking_path,
                                        meta_data=meta_data,
                                        preprocess_method="UIED",
                                        match_id = match_id,
                                        max_workers=max_workers).preprocessing()

print(df_bepro.head())