This package includes several of the functions I use to analyze football data. More functions will be added as I get everything situated.
Currently, I just have functions to generated style/profile similarities between players, teams, and leagues. As this is the first time I'm publishing a package, I needed to start small.
Similarity scores are purely based on the style, not quality of a player, league, or team. They are generated from over 35 variales (all z-scores so they are the same scale) and use Cosine Similarity. This is a pretty standard method, and StatsBomb uses Cosine Similarity as one method for their similar player search.
To install the package, you can type or copy/paste this into you command line:
pip install git+https://github.com/griffisben/griffis_soccer_analysis.gitIf you want to update the package, you can first uninstall the old package by entering this line into your command line:
pip uninstall griffis_soccer_analysisHere is an example of how to load the package.
from griffis_soccer_analysis.fbref_code import *
from griffis_soccer_analysis.similarity import *
from griffis_soccer_analysis.whoscored_match_report import *Please note that you are welcome to take some of the raw code and modify to your needs. If you do that, though, please ensure that you give me credit no matter how much you modify. thank you.
Download a csv of events for a given match on the Detailed Tournaments section of Whoscored, and make a post-match report image
whoscored_match_report(
# The URL of the Whoscored match (detailed tournamnets only)
url = 'https://www.whoscored.com/Matches/1735525/Live/Belgium-Jupiler-Pro-League-2023-2024-Union-St-Gilloise-Eupen',
# The match name
match = 'Union St. Gilloise 4-1 Eupen',
# Home team name
team_h = 'Union St. Gilloise',
# Whoscored's ID for the home team (can find in the html slug of the team's page)
teamId_h = 2647,
# Away team name
team_a = 'Eupen',
# Away team ID
teamId_a = 2166,
# League name
lg = 'Belgian Pro League',
# Game date
date = 'Oct 20, 2023',
# Your signature
sig = '@BeGriffis',
# Language for the image (English, Spanish, Portuguese, French)
language = 'English',
# Location of your Chromedriver
chrome_driver_loc = r"C:\Users\Ben\chromedriver.exe",
# Where you want the data to download to
data_download_loc = "C:/users/ben/downloads",
# Where you want the final image to export to
img_save_loc = "C:/users/ben/downloads",
# Path to home team's image
home_img = "C:/users/ben/club images/Union St. Gilloise.png",
# Path to away team's image
away_img = "C:/users/ben/club images/Eupen.png"
)Download the latest FBRef data (via Opta) for UEFA top 5 players (note, will only work for the current, ongoing season)
scrape_fbref_t5_leagues_players(season = '2023-2024')Download the latest FBRef data (via Opta) for all other leagues FBRef has advanced data for (note, will only work for the current, ongoing season)
# Comment out any leagues (and their respective season) that you don't want to download
# Also make sure to update the season if a change in season occurs... only changes file name, not the season that's scraped
comps = [
'2. Bundesliga',
'Ligue 2',
'Serie B',
'La Liga 2',
'Belgian Pro League',
'Liga MX',
'Eredivisie',
'Primeira Liga',
'Championship',
'Argentine Primera División',
'Argentina Copa de la Liga'
'MLS',
'Brasileirão',
'NWSL',
'WSL',
'Liga F',
'A-League Women',
'Division 1 Féminine',
'Frauen-Bundesliga',
'Serie A Femminile'
]
ssns = [
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2024',
'2024',
'2024',
'2024',
'2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024',
'2023-2024'
]
scrape_fbref_next12_leagues_players(comps = comps, seasons = ssns)combine_t5_next12_fbref(t5_season = '2023-2024')fbref_scout_report(season = '2023-2024',
program = 'mf', # gk, df, mf, fw (each has 12 selected metrics for the position)
player_pos = 'MF', # GK, DF, MF, FW (FBRef positions)
playerPrompt = 'Lucas Besozzi',
SquadPrompt = '',
minutesPlayed = 700,
compP = 'Argentine Primera División',
saveinput = 'n',
signature = '@BeGriffis',
data_date = 'Data as of 9/29/23',
fbref_file_path = 'C:/Users/Ben/From Mac/Python/'
)For a Juypter Notebook file with example code & output and more info on each variable, please see this file
available_leagues()teams_in_league(
league = "A-League Men 22-23"
)available_players(
league = "A-League Men 22-23",
team = 'Western Sydney Wanderers'
)# This is how to grab all outputs
df, info, dist_fig = league_similarity(
league = "A-League Men",
season = "22-23",
nlgs = 15
)
# This is one way to print the information and then the similarity dataframe
for i in range(len(info)):
print(info[i])
df# This is how to grab all outputs
df, info, dist_fig = team_similarity(
team = "Western Sydney Wanderers",
league = "A-League Men",
season = "22-23",
nlgs = 15
)
# This is one way to print the information and then the similarity dataframe
for i in range(len(info)):
print(info[i])
df"""
CHOICES FOR compare_league VARIABLE
All
UEFA T5
UEFA Next 10
UEFA Next 20
UEFA T5 2nd Tiers
8 AFC
CONMEBOL Top 4
Argentina & Brazil
Scandinavia (note: this only includes top tiers from Denmark, Norway, Sweden, and Finland)
Or, make your own by writing each league-season name (can find with available_leagues()) separated by a space & comma:
'MLS 2023, Liga MX 23-24'
'Premier League 23-24, Championship 23-24, League One 23-24, League Two 23-24'
"""
df, info, dist_fig = player_similarity(
player = "E. Spertsyan (23, Krasnodar, Russian Premier League 23-24)",
position = "CM",
nplayers = 20,
compare_leagues = 'All',
min_age = 16, # Minimum age of similar players (always still inside the top 5%)
max_age = 40, # Maximum age of similar players (always still inside the top 5%)
similar_lg_team = False,
mean_sim = False,
)
# This is one way to print the information and then the similarity dataframe
for i in range(len(info)):
print(info[i])
df
