Jeg har opdateret scriptet en smule. Se evt. Xg-plots med Python og Superliga.dk (part2).
Man kan nu vælge når man kører scriptet, hvilke runder man ønsker data fra og evt. tilføje et enkelt holds ID, så man kun får kampe hvor holdet har været indvolveret.
import os
import requests
from PIL import Image
import json
import pandas as pd
import matplotlib.pyplot as plt
from mplsoccer import Pitch
import time
# List of teams and their IDs
teams = [
{"teamId": 8071, "teamName": "AGF"},
{"teamId": 8470, "teamName": "AaB"},
{"teamId": 8595, "teamName": "Brøndby IF"},
{"teamId": 8391, "teamName": "F.C. København"},
{"teamId": 8113, "teamName": "FC Midtjylland"},
{"teamId": 10202, "teamName": "FC Nordsjælland"},
{"teamId": 9907, "teamName": "Lyngby Boldklub"},
{"teamId": 8410, "teamName": "Randers FC"},
{"teamId": 8415, "teamName": "Silkeborg IF"},
{"teamId": 8487, "teamName": "Sønderjyske Fodbold"},
{"teamId": 8231, "teamName": "Vejle Boldklub"},
{"teamId": 9939, "teamName": "Viborg FF"},
{"teamId": 9814, "teamName": "AC Horsens"},
{"teamId": 8453, "teamName": "B.93"},
{"teamId": 8285, "teamName": "Esbjerg fB"},
{"teamId": 9821, "teamName": "FC Roskilde"},
{"teamId": 8454, "teamName": "FC Fredericia"},
{"teamId": 8289, "teamName": "HB Køge"},
{"teamId": 9940, "teamName": "Hillerød Fodbold"},
{"teamId": 9950, "teamName": "Hobro IK"},
{"teamId": 10240, "teamName": "Hvidovre IF"},
{"teamId": 6308, "teamName": "Kolding IF"},
{"teamId": 8414, "teamName": "OB"},
{"teamId": 8615, "teamName": "Vendsyssel FF"}
]
# Print the list of teams and their IDs
print("Team IDs and Names:")
for team in teams:
print(f"{team['teamId']}: {team['teamName']}")
# Function to parse rounds input and handle ranges
def parse_rounds(rounds_input):
rounds = []
for part in rounds_input.split(','):
if '-' in part:
start, end = part.split('-')
rounds.extend(range(int(start), int(end) + 1))
else:
rounds.append(int(part))
return [str(round) for round in rounds] # Convert rounds to strings for comparison
# Prompt user to input the rounds they want to fetch event IDs for
rounds_input = input("Enter the rounds you want to fetch event IDs for (comma separated, e.g., 1,2,5 or 1-5): ")
selected_rounds = parse_rounds(rounds_input)
# Prompt user to input the team ID if they want to filter by a specific team (leave blank if not)
team_id = input("Enter the team ID to filter by (leave blank if not applicable): ").strip()
# Step 1: Fetch events
events_url = 'https://api.superliga.dk/events-v2?appName=dk.releaze.livecenter.spdk&access_token=5b6ab6f5eb84c60031bbbd24&env=production&locale=da&seasonId=20962'
response = requests.get(events_url)
events_data = response.json()
# Step 2: Filter finished events from selected rounds and optional team ID
finished_events = [
event for event in events_data['events']
if event['statusType'] == 'finished' and event['round'] in selected_rounds
and (not team_id or event['homeId'] == int(team_id) or event['awayId'] == int(team_id))
]
print(f"Total finished events found: {len(finished_events)}")
def generate_plots(event_id):
try:
details_url = f'https://api.superliga.dk/opta-stats/event/{event_id}/detail-expected-goals?appName=superligadk&access_token=5b6ab6f5eb84c60031bbbd24&env=production&locale=da'
response = requests.get(details_url)
data = response.json()
home_team_id = data.get('homeId')
away_team_id = data.get('awayId')
# Paths to team logos
logo_directory = '/home/xxxxxx/python_scripts/TeamPngs' # Update with the path to your logos directory
home_team_logo_path = os.path.join(logo_directory, f'{home_team_id}.png')
away_team_logo_path = os.path.join(logo_directory, f'{away_team_id}.png')
# Load team logos
home_team_logo = Image.open(home_team_logo_path)
away_team_logo = Image.open(away_team_logo_path)
# Extract home and away expected goals data
home_data = pd.DataFrame(data['expectedGoalsData']['home'])
away_data = pd.DataFrame(data['expectedGoalsData']['away'])
# Extract scores
home_score = data['score']['home']
away_score = data['score']['away']
# Adjusting the coordinates assuming the pitch is 120x80
home_data['x_adjusted'] = home_data['x'] * 120 / 100
home_data['y_adjusted'] = home_data['y'] * 80 / 100
away_data['x_adjusted'] = away_data['x'] * 120 / 100
away_data['y_adjusted'] = away_data['y'] * 80 / 100
# Identify the goals in home and away data
home_goals = home_data[home_data['type'] == 'goal'].copy()
away_goals = away_data[away_data['type'] == 'goal'].copy()
# Set up the pitch
pitch = Pitch(pitch_length=120, pitch_width=80, pitch_color='grass', line_color='white', goal_type='box')
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))
# Plot home team shots and table
pitch.draw(ax=axes[0])
axes[0].set_title(f"{data['homeName']} ({home_score}) - Expected Goals", fontsize=20)
sc1 = pitch.scatter(home_data['x_adjusted'], home_data['y_adjusted'], s=home_data['expectedGoalsValue']*1000, c='red', label='Home xG', alpha=0.6, edgecolors='black', ax=axes[0])
# Add goal markers only if there are goals
if not home_goals.empty:
sc1_goals = pitch.scatter(home_goals['x_adjusted'], home_goals['y_adjusted'], s=home_goals['expectedGoalsValue']*1000, c='gold', label='Home Goals', alpha=1, edgecolors='black', ax=axes[0])
# Sort home xG data in descending order
top_home_xg = home_data.nlargest(5, "expectedGoalsValue").copy()
# Combine first name and last name for the table
top_home_xg['playerName'] = top_home_xg['firstName'] + ' ' + top_home_xg['lastName']
top_home_xg = top_home_xg[["expectedGoalsValue", "playerName"]] # Select relevant columns
# Add goal scorer to the top xG chances if applicable
if not home_goals.empty:
home_goals['playerName'] = home_goals['firstName'] + ' ' + home_goals['lastName']
home_goals = home_goals[["expectedGoalsValue", "playerName"]]
top_home_xg = pd.concat([home_goals, top_home_xg]).drop_duplicates().head(5)
# Display top 5 home xG chances in a table below the plot
table_home = axes[0].table(cellText=top_home_xg.values, colLabels=top_home_xg.columns, loc='bottom', cellLoc='center')
# Adjust table properties for better readability
table_home.auto_set_font_size(False)
table_home.set_fontsize(10)
table_home.scale(1, 1.5)
# Make goal-scoring opportunities bold
for key, cell in table_home.get_celld().items():
if key[0] > 0 and key[1] == 0: # data cells in first column (xG values)
if table_home[key].get_text().get_text() in home_goals['expectedGoalsValue'].astype(str).values:
table_home[key].set_fontsize(12)
table_home[key].set_text_props(fontweight='bold')
# Plot away team shots and table
pitch.draw(ax=axes[1])
axes[1].set_title(f"{data['awayName']} ({away_score}) - Expected Goals", fontsize=20)
sc2 = pitch.scatter(away_data['x_adjusted'], away_data['y_adjusted'], s=away_data['expectedGoalsValue']*1000, c='blue', label='Away xG', alpha=0.6, edgecolors='black', ax=axes[1])
# Add goal markers only if there are goals
if not away_goals.empty:
sc2_goals = pitch.scatter(away_goals['x_adjusted'], away_goals['y_adjusted'], s=away_goals['expectedGoalsValue']*1000, c='gold', label='Away Goals', alpha=1, edgecolors='black', ax=axes[1])
# Sort away xG data in descending order
top_away_xg = away_data.nlargest(5, "expectedGoalsValue").copy()
# Combine first name and last name for the table
top_away_xg['playerName'] = top_away_xg['firstName'] + ' ' + top_away_xg['lastName']
top_away_xg = top_away_xg[["expectedGoalsValue", "playerName"]] # Select relevant columns
# Add goal scorer to the top xG chances if applicable
if not away_goals.empty:
away_goals['playerName'] = away_goals['firstName'] + ' ' + away_goals['lastName']
away_goals = away_goals[["expectedGoalsValue", "playerName"]]
top_away_xg = pd.concat([away_goals, top_away_xg]).drop_duplicates().head(5)
# Display top 5 away xG chances in a table below the plot
table_away = axes[1].table(cellText=top_away_xg.values, colLabels=top_away_xg.columns, loc='bottom', cellLoc='center')
# Adjust table properties for better readability
table_away.auto_set_font_size(False)
table_away.set_fontsize(10)
table_away.scale(1, 1.5)
# Make goal-scoring opportunities bold
for key, cell in table_away.get_celld().items():
if key[0] > 0 and key[1] == 0: # data cells in first column (xG values)
if table_away[key].get_text().get_text() in away_goals['expectedGoalsValue'].astype(str).values:
table_away[key].set_fontsize(12)
table_away[key].set_text_props(fontweight='bold')
# Add legends (moved above tables for better visibility)
axes[0].legend(loc='upper left', fontsize=10)
axes[1].legend(loc='upper left', fontsize=10)
# Add team logos near team names
ax_logo_home = fig.add_axes([0.07, 0.8, 0.1, 0.1], anchor='NE', zorder=1)
ax_logo_home.imshow(home_team_logo)
ax_logo_home.axis('off')
ax_logo_away = fig.add_axes([0.52, 0.8, 0.1, 0.1], anchor='NE', zorder=1)
ax_logo_away.imshow(away_team_logo)
ax_logo_away.axis('off')
# Add a decal (text annotation) in the final figure
fig.text(0.5, 0.95, 'EXPECTED GOALS ANALYSIS', ha='center', va='center', fontsize=38, fontweight='bold', color='blue')
# Set up filename and directory for saving the plot
output_directory = '/home/xxxxx/R_Superliga' # Specify your directory here
os.makedirs(output_directory, exist_ok=True) # Create directory if it doesn't exist
home_name = data['homeName'].replace(" ", "_") # Replace spaces with underscores for filename
away_name = data['awayName'].replace(" ", "_") # Replace spaces with underscores for filename
filename = f'{home_name}_vs_{away_name}.png'
# Save the plot
plt.savefig(os.path.join(output_directory, filename), bbox_inches='tight')
# Optional: Close the plot to free up memory
plt.close(fig)
except Exception as e:
print(f"An error occurred while processing event ID {event_id}: {e}")
# Iterate over the finished events and generate plots
for event in finished_events:
print(f"Processing event ID: {event['eventId']}")
generate_plots(event['eventId'])
time.sleep(1) # Add a delay to avoid hitting the rate limit