Installation
## Use the package manager [pip](pypi.org) to install
pip install SoccerViz==0.6.0
Import the SoccerViz Package
#SoccerViz Package
from SoccerViz import plot,extract,datafilter
Scrape event data from **[WhoScored](whoscored.com)** **_ONLY_** inorder to use it for data analysis and visualization, you would have to fill in the following parameters according to
your liking, below example is given follow it and keep in mind the instructions given too with the code.
#This is an example URL from Whoscored.com similar to the below one
url = 'https://www.whoscored.com/Matches/1821060/Live/England-Premier-League-2024-2025-Aston-Villa-Arsenal' #This is an example URL from Whoscored.com
understat_url = 'https://understat.com/match/26618' #Copy Link of the Understat URL
#Call the extract functions to get Pass Dataframe,Players Dataframe of the particular match
df = extract.pass_data(url)
players_df = extract.player_data(url)
home_df,away_df = extract.shots(understat_url)
#You can find the TEAM Id's of the clubs on their WhoScored page html tags by clicking on the club logos
home_team_id=24 #Arsenal Team ID
away_team_id=13 #Aston Villa Team ID
home_team_name = 'Arsenal'
away_team_name = 'Aston Villa'
After scrapping the event data and assembling into DataFrames, you will need to filter the data according to the teams and players.
#Filter all the data according to the teams by calling the function into DataFrames(their names are pretty self explanatory)
#You can use and call any dataframe you would like to analyze in raw tables and columns i.e. in a DataFrame form
pass_between_home, pass_between_away, avg_loc_home, avg_loc_away, passes_home, passes_away,df_prg_home,df_comp_prg_home,df_uncomp_prg_home,df_prg_away,df_comp_prg_away,df_uncomp_prg_away = datafilter.analyze_passes(df, players_df, home_team_id, away_team_id)
#Same goes for the shots of the match
df1_missed,df2_missed,df1_saved,df2_saved,df1_goal,df2_goal,df1_block,df2_block,totalxG1,totalxG2=datafilter.analyze_shots(home_df,away_df)
Now finally, you can plot the pass network map for both the teams to analyze and visualize by calling the function
## Plotting Pass Network
#Call the function and manually put in the home and away team names
plot = plot.pass_network(pass_between_home, pass_between_away, avg_loc_home,avg_loc_away,home_team_name,away_team_name)
## Plotting Prg Passes
#Call the function and put in home and away team names
plot = plot.prg_passes(df_comp_prg_home, df_uncomp_prg_home, df_comp_prg_away, df_uncomp_prg_away, home_team_name,
away_team_name)
## Plotting Shot Maps
#Call the function
plot = plot.shot_map(df1_missed, df2_missed, df1_saved, df2_saved, df1_goal, df2_goal, df1_block, df2_block, home_team_name,
away_team_name,totalxG1, totalxG2)
## Compare Players from the Big 5 EU Leagues 2022-2024 seasons using Pizza Plots
First and foremost download the CSV File 'big5stats22-24.csv' *data from FBRef via WorldFootballR
Or you can download from the GitHub>DATA
#Load the CSV file as DF
df = pd.read_csv('#filepath')
The available parameters are:
['Gls', 'Ast', 'G+A', 'xG', 'PrgC', 'PrgP', 'PrgR', 'Shots', 'Shts on Target',
'TklW', 'Blocks', 'Int', 'Succ Takeons', 'Recov', 'Aerial Duels', 'Pass CmpRate',
'LongPass CmpRate', 'KeyPasses', 'PassPA', 'FinalThird Passes', 'CrsPA']
Positions Values should be coherent with these:
'DF', 'MF', 'FW', 'GK'
Example Usage
position = 'DF' #specify the position
player1 = 'Riccardo Calafiori' # double-check spellings from FBRef
player2 = 'Ben White' # double-check special characters in player names
#specify any parameters from the list above to compare players
params = ['G+A', 'PrgC','PrgP', 'TklW','Blocks','Int','LongPass CmpRate','KeyPasses','CrsPA']
values, values2 = datafilter.compare_players(df, position, player1, player2, params)
plot = plot.pizza_plot(values, values2, params, player1, player2)
## Plot ShotMaps for Individual Players throughout the Season
playerid="5220" #put the player Id in strings from Understat.com URL
season=2023 #season you wish to plot the shotmap of
player_name = "Kai Havertz" #player_name
plot = plot.playershots(playerid,season,player_name) #Call in the function to plot
## Credits
Huge Shoutout to the guys at Mplsoccer, do check their package out also, and also checkout Mckay Johns Youtube Channel, which helped me alot in learning python and football analytics
And please don't forget to drop me feedback on Twitter/X, @athalakbar13.
Enjoy!!
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"description": "Installation\n\n## Use the package manager [pip](pypi.org) to install \n\n\n pip install SoccerViz==0.6.0\n\n\nImport the SoccerViz Package\n\n #SoccerViz Package\n from SoccerViz import plot,extract,datafilter\n\nScrape event data from **[WhoScored](whoscored.com)** **_ONLY_** inorder to use it for data analysis and visualization, you would have to fill in the following parameters according to\nyour liking, below example is given follow it and keep in mind the instructions given too with the code.\n \n \n #This is an example URL from Whoscored.com similar to the below one\n \n url = 'https://www.whoscored.com/Matches/1821060/Live/England-Premier-League-2024-2025-Aston-Villa-Arsenal' #This is an example URL from Whoscored.com\n understat_url = 'https://understat.com/match/26618' #Copy Link of the Understat URL\n\n\n #Call the extract functions to get Pass Dataframe,Players Dataframe of the particular match\n \n \n df = extract.pass_data(url)\n players_df = extract.player_data(url)\n home_df,away_df = extract.shots(understat_url)\n \n #You can find the TEAM Id's of the clubs on their WhoScored page html tags by clicking on the club logos\n \n home_team_id=24 #Arsenal Team ID\n away_team_id=13 #Aston Villa Team ID\n home_team_name = 'Arsenal'\n away_team_name = 'Aston Villa'\n\n\nAfter scrapping the event data and assembling into DataFrames, you will need to filter the data according to the teams and players.\n\n #Filter all the data according to the teams by calling the function into DataFrames(their names are pretty self explanatory)\n\n #You can use and call any dataframe you would like to analyze in raw tables and columns i.e. in a DataFrame form\n pass_between_home, pass_between_away, avg_loc_home, avg_loc_away, passes_home, passes_away,df_prg_home,df_comp_prg_home,df_uncomp_prg_home,df_prg_away,df_comp_prg_away,df_uncomp_prg_away = datafilter.analyze_passes(df, players_df, home_team_id, away_team_id)\n\n #Same goes for the shots of the match\n df1_missed,df2_missed,df1_saved,df2_saved,df1_goal,df2_goal,df1_block,df2_block,totalxG1,totalxG2=datafilter.analyze_shots(home_df,away_df)\n\n\nNow finally, you can plot the pass network map for both the teams to analyze and visualize by calling the function\n## Plotting Pass Network\n #Call the function and manually put in the home and away team names\n \n plot = plot.pass_network(pass_between_home, pass_between_away, avg_loc_home,avg_loc_away,home_team_name,away_team_name)\n \n\n\n## Plotting Prg Passes\n \n #Call the function and put in home and away team names\n plot = plot.prg_passes(df_comp_prg_home, df_uncomp_prg_home, df_comp_prg_away, df_uncomp_prg_away, home_team_name,\n away_team_name)\n\n\n\n## Plotting Shot Maps\n \n #Call the function \n plot = plot.shot_map(df1_missed, df2_missed, df1_saved, df2_saved, df1_goal, df2_goal, df1_block, df2_block, home_team_name,\n away_team_name,totalxG1, totalxG2)\n \n\n\n\n## Compare Players from the Big 5 EU Leagues 2022-2024 seasons using Pizza Plots \n\n First and foremost download the CSV File 'big5stats22-24.csv' *data from FBRef via WorldFootballR\n Or you can download from the GitHub>DATA\n #Load the CSV file as DF\n df = pd.read_csv('#filepath')\n\nThe available parameters are:\n ['Gls', 'Ast', 'G+A', 'xG', 'PrgC', 'PrgP', 'PrgR', 'Shots', 'Shts on Target', \n 'TklW', 'Blocks', 'Int', 'Succ Takeons', 'Recov', 'Aerial Duels', 'Pass CmpRate', \n 'LongPass CmpRate', 'KeyPasses', 'PassPA', 'FinalThird Passes', 'CrsPA']\n\nPositions Values should be coherent with these:\n 'DF', 'MF', 'FW', 'GK' \n\nExample Usage\n\n position = 'DF' #specify the position\n player1 = 'Riccardo Calafiori' # double-check spellings from FBRef\n player2 = 'Ben White' # double-check special characters in player names\n \n #specify any parameters from the list above to compare players\n params = ['G+A', 'PrgC','PrgP', 'TklW','Blocks','Int','LongPass CmpRate','KeyPasses','CrsPA']\n\n values, values2 = datafilter.compare_players(df, position, player1, player2, params)\n plot = plot.pizza_plot(values, values2, params, player1, player2)\n\n\n## Plot ShotMaps for Individual Players throughout the Season\n\n playerid=\"5220\" #put the player Id in strings from Understat.com URL\n season=2023 #season you wish to plot the shotmap of\n player_name = \"Kai Havertz\" #player_name\n \n plot = plot.playershots(playerid,season,player_name) #Call in the function to plot\n \n\n\n\n\n## Credits\n\nHuge Shoutout to the guys at Mplsoccer, do check their package out also, and also checkout Mckay Johns Youtube Channel, which helped me alot in learning python and football analytics\n \nAnd please don't forget to drop me feedback on Twitter/X, @athalakbar13.\n\nEnjoy!!\n\n \n",
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